ALBERT

Description: Motivation The traditional view of cancer evolution states that a cancer genome accumulates a sequential ordering of mutations over a long period of time. However, in recent years it has been suggested that a cancer genome may instead undergo a one-time catastrophic event, such as chromothripsis , where a large number of mutations instead occur simultaneously . A number of potential signatures of chromothripsis have been proposed. In this work, we provide a rigorous formulation and analysis of the ‘ability to walk the derivative chromosome’ signature originally proposed by Korbel and Campbell. In particular, we show that this signature, as originally envisioned, may not always be present in a chromothripsis genome and we provide a precise quantification of under what circumstances it would be present. We also propose a variation on this signature, the H/T alternating fraction , which allows us to overcome some of the limitations of the original signature. Results We apply our measure to both simulated data and a previously analyzed real cancer dataset and find that the H/T alternating fraction may provide useful signal for distinguishing genomes having acquired mutations simultaneously from those acquired in a sequential fashion. Availability and implementation An implementation of the H/T alternating fraction is available at https://bitbucket.org/oesperlab/ht-altfrac . Contact loesper@carleton.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites. Results Tested on four different datasets, CCmiR predicted miRNA target sites with a high recall and a reasonable precision, and identified known and new cooperative and competitive miRNAs supported by literature. Compared with three state-of-the-art computational methods, CCmiR had a higher recall and a higher precision. Availability and implementation CCmiR is freely available at http://hulab.ucf.edu/research/projects/miRNA/CCmiR . Contact xiaoman@mail.ucf.edu or haihu@cs.ucf.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Eukaryotic chromosomes adapt a complex and highly dynamic three-dimensional (3D) structure, which profoundly affects different cellular functions and outcomes including changes in epigenetic landscape and in gene expression. Making the scenario even more complex, cancer cells harbor chromosomal abnormalities [e.g. copy number variations (CNVs) and translocations] altering their genomes both at the sequence level and at the level of 3D organization. High-throughput chromosome conformation capture techniques (e.g. Hi-C), which are originally developed for decoding the 3D structure of the chromatin, provide a great opportunity to simultaneously identify the locations of genomic rearrangements and to investigate the 3D genome organization in cancer cells. Even though Hi-C data has been used for validating known rearrangements, computational methods that can distinguish rearrangement signals from the inherent biases of Hi-C data and from the actual 3D conformation of chromatin, and can precisely detect rearrangement locations de novo have been missing. Results In this work, we characterize how intra and inter-chromosomal Hi-C contacts are distributed for normal and rearranged chromosomes to devise a new set of algorithms (i) to identify genomic segments that correspond to CNV regions such as amplifications and deletions ( HiCnv ), (ii) to call inter-chromosomal translocations and their boundaries ( HiCtrans ) from Hi-C experiments and (iii) to simulate Hi-C data from genomes with desired rearrangements and abnormalities ( AveSim ) in order to select optimal parameters for and to benchmark the accuracy of our methods. Our results on 10 different cancer cell lines with Hi-C data show that we identify a total number of 105 amplifications and 45 deletions together with 90 translocations, whereas we identify virtually no such events for two karyotypically normal cell lines. Our CNV predictions correlate very well with whole genome sequencing data among chromosomes with CNV events for a breast cancer cell line ( r  = 0.89) and capture most of the CNVs we simulate using Avesim. For HiCtrans predictions, we report evidence from the literature for 30 out of 90 translocations for eight of our cancer cell lines. Furthermore, we show that our tools identify and correctly classify relatively understudied rearrangements such as double minutes and homogeneously staining regions. Considering the inherent limitations of existing techniques for karyotyping (i.e. missing balanced rearrangements and those near repetitive regions), the accurate identification of CNVs and translocations in a cost-effective and high-throughput setting is still a challenge. Our results show that the set of tools we develop effectively utilize moderately sequenced Hi-C libraries (100–300 million reads) to identify known and de novo chromosomal rearrangements/abnormalities in well-established cancer cell lines. With the decrease in required number of cells and the increase in attainable resolution, we believe that our framework will pave the way towards comprehensive mapping of genomic rearrangements in primary cells from cancer patients using Hi-C. Availability and implementation CNV calling: https://github.com/ay-lab/HiCnv , Translocation calling: https://github.com/ay-lab/HiCtrans and Hi-C simulation: https://github.com/ay-lab/AveSim . Contact ferhatay@lji.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Cancer hallmarks, a concept that seeks to explain the complexity of cancer initiation and development, provide a new perspective of studying cancer signaling which could lead to a greater understanding of this complex disease. However, to the best of our knowledge, there is currently a lack of tools that support such hallmark-based study of the cancer signaling network, thereby impeding the gain of knowledge in this area. We present TROVE, an user-friendly software that facilitates hallmark annotation, visualization and analysis in cancer signaling networks. In particular, TROVE facilitates hallmark analysis specific to particular cancer types. Availability and implementation Available under the Eclipse Public License from: https://sites.google.com/site/cosbyntu/softwares/trove and https://github.com/trove2017/Trove . Contact hechua@ntu.edu.sg or assourav@ntu.edu.sg

Description: Summary High-throughput screening of the host transcriptional response to various viral infections provides a wealth of data, but utilization of microarray and next generation sequencing (NGS) data for analysis can be difficult. The Ho st T ranscriptional R esponse D ata B ase (HoTResDB), allows visitors to access already processed microarray and NGS data from non-human primate models of viral hemorrhagic fever to better understand the host transcriptional response. Availability HoTResDB is freely available at http://hotresdb.bu.edu Contact jhconnor@bu.edu

Description: Motivation Structural variation, including large deletions, duplications, inversions, translocations and other rearrangements, is common in human and cancer genomes. A number of methods have been developed to identify structural variants from Illumina short-read sequencing data. However, reliable identification of structural variants remains challenging because many variants have breakpoints in repetitive regions of the genome and thus are difficult to identify with short reads. The recently developed linked-read sequencing technology from 10X Genomics combines a novel barcoding strategy with Illumina sequencing. This technology labels all reads that originate from a small number (∼5 to 10) DNA molecules ∼50 Kbp in length with the same molecular barcode. These barcoded reads contain long-range sequence information that is advantageous for identification of structural variants. Results We present Novel Adjacency Identification with Barcoded Reads (NAIBR), an algorithm to identify structural variants in linked-read sequencing data. NAIBR predicts novel adjacencies in an individual genome resulting from structural variants using a probabilistic model that combines multiple signals in barcoded reads. We show that NAIBR outperforms several existing methods for structural variant identification—including two recent methods that also analyze linked-reads—on simulated sequencing data and 10X whole-genome sequencing data from the NA12878 human genome and the HCC1954 breast cancer cell line. Several of the novel somatic structural variants identified in HCC1954 overlap known cancer genes. Availability and implementation Software is available at compbio.cs.brown.edu/software . Contact braphael@princeton.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Cancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. The decomposition of a cancer genome’s mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to be able to not only decompose a patient’s mutational profile into signatures but also establish the accuracy of such decomposition. Results We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified both very stable and highly unstable signatures, as well as signatures that previously have not been associated with breast cancer. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. Availability and implementation All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi\#signatureestimation . Contact wojtowda@ncbi.nlm.nih.gov or przytyck@ncbi.nlm.nih.gov Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Brain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ 1 -norm or its variants to induce sparsity. The ℓ 0 -norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem. Results In this paper, we propose the truncated ℓ 1 -norm penalized SCCA to improve the performance and effectiveness of the ℓ 1 -norm based SCCA methods. Besides, we propose an efficient optimization algorithms to solve this novel SCCA problem. The proposed method is an adaptive shrinkage method via tuning τ . It can avoid the time intensive parameter tuning if given a reasonable small τ . Furthermore, we extend it to the truncated group-lasso (TGL), and propose TGL-SCCA model to improve the group-lasso-based SCCA methods. The experimental results, compared with four benchmark methods, show that our SCCA methods identify better or similar correlation coefficients, and better canonical loading profiles than the competing methods. This demonstrates the effectiveness and efficiency of our methods in discovering interesting imaging genetic associations. Availability and implementation The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/tlpscca/ . Contact dulei@nwpu.edu.cn or shenli@iu.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Modelling with multiple servers that use different algorithms for docking results in more reliable predictions of interaction sites. However, the scoring and comparison of all models by an expert is time-consuming and is not feasible for large volumes of data generated by such modelling. Results Quality ASsessment of DOcking Models (QASDOM) Server is a simple and efficient tool for real-time simultaneous analysis, scoring and ranking of data sets of receptor–ligand complexes built by a range of docking techniques. This meta-server is designed to analyse large data sets of docking models and rank them by scoring criteria developed in this study. It produces two types of output showing the likelihood of specific residues and clusters of residues to be involved in receptor–ligand interactions and the ranking of models. The server also allows visualizing residues that form interaction sites in the receptor and ligand sequence and displays 3D model structures of the receptor–ligand complexes. Availability http://qasdom.eimb.ru . Contact alexei.adzhubei@eimb.ru. Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods. Results On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent. Availability and implementation IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/ . Contact andrew@bioinf.org.uk or andrew.martin@ucl.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Protein function is directly related to amino acid residue composition and the dynamics of these residues. Centrality analyses based on residue interaction networks permit to identify key residues in a protein that are important for its fold or function. Such central residues and their environment constitute suitable targets for mutagenesis experiments. Predicted flexibility and changes in flexibility upon mutation provide valuable additional information for the design of such experiments. Results We combined centrality analyses with DynaMine flexibility predictions in a Cytoscape app called RINspector. The app performs centrality analyses and directly visualizes the results on a graph of predicted residue flexibility. In addition, the effect of mutations on local flexibility can be calculated. Availability and implementation The app is publicly available in the Cytoscape app store. Contact guillaume.brysbaert@univ-lille1.fr Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Fully exploiting the wealth of data in current bacterial population genomics datasets requires synthesizing and integrating different types of analysis across millions of base pairs in hundreds or thousands of isolates. Current approaches often use static representations of phylogenetic, epidemiological, statistical and evolutionary analysis results that are difficult to relate to one another. Phandango is an interactive application running in a web browser allowing fast exploration of large-scale population genomics datasets combining the output from multiple genomic analysis methods in an intuitive and interactive manner. Availability and implementation Phandango is a web application freely available for use at www.phandango.net and includes a diverse collection of datasets as examples. Source code together with a detailed wiki page is available on GitHub at https://github.com/jameshadfield/phandango . Contact jh22@sanger.ac.uk or sh16@sanger.ac.uk

Description: Summary MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context. Availability and implementation Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/ Contact contact-metexplore@inra.fr

Description: Motivation Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired. Results We developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development. Availability and implementation MATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES . The single cell THP-1 and T2EC transcriptional profiles are available from the original publications ( Kouno et al. , 2013 ; Richard et al. , 2016 ). The in silico single cell data are available on SINCERITIES websites. Contact rudi.gunawan@chem.ethz.ch Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Addressing deleterious effects of noncoding mutations is an essential step towards the identification of disease-causal mutations of gene regulatory elements. Several methods for quantifying the deleteriousness of noncoding mutations using artificial intelligence, deep learning and other approaches have been recently proposed. Although the majority of the proposed methods have demonstrated excellent accuracy on different test sets, there is rarely a consensus. In addition, advanced statistical and artificial learning approaches used by these methods make it difficult porting these methods outside of the labs that have developed them. To address these challenges and to transform the methodological advances in predicting deleterious noncoding mutations into a practical resource available for the broader functional genomics and population genetics communities, we developed SNPDelScore, which uses a panel of proposed methods for quantifying deleterious effects of noncoding mutations to precompute and compare the deleteriousness scores of all common SNPs in the human genome in 44 cell lines. The panel of deleteriousness scores of a SNP computed using different methods is supplemented by functional information from the GWAS Catalog, libraries of transcription factor-binding sites, and genic characteristics of mutations. SNPDelScore comes with a genome browser capable of displaying and comparing large sets of SNPs in a genomic locus and rapidly identifying consensus SNPs with the highest deleteriousness scores making those prime candidates for phenotype-causal polymorphisms. Availability and implementation https://www.ncbi.nlm.nih.gov/research/snpdelscore/ Contact ovcharen@nih.gov Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation The selection of a single nucleotide polymorphism (SNP) using bibliographic methods can be a very time-consuming task. Moreover, a SNP selected in this way may not be easily visualized in its genomic context by a standard user hoping to correlate it with other valuable information. Here we propose a web form built on top of Circos that can assist SNP-centered screening, based on their location in the genome and the regulatory modules they can disrupt. Its use may allow researchers to prioritize SNPs in genotyping and disease studies. Results SiNoPsis is bundled as a web portal. It focuses on the different structures involved in the genomic expression of a gene, especially those found in the core promoter upstream region. These structures include transcription factor binding sites (for promoter and enhancer signals), histones and promoter flanking regions. Additionally, the tool provides eQTL and linkage disequilibrium (LD) properties for a given SNP query, yielding further clues about other indirectly associated SNPs. Possible disruptions of the aforementioned structures affecting gene transcription are reported using multiple resource databases. SiNoPsis has a simple user-friendly interface, which allows single queries by gene symbol, genomic coordinates, Ensembl gene identifiers, RefSeq transcript identifiers and SNPs. It is the only portal providing useful SNP selection based on regulatory modules and LD with functional variants in both textual and graphic modes (by properly defining the arguments and parameters needed to run Circos). Availability and implementation SiNoPsis is freely available at https://compgen.bio.ub.edu/SiNoPsis/ Contact danielboloc@gmail.com Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Inter-residue contacts in proteins have been widely acknowledged to be valuable for protein 3 D structure prediction. Accurate prediction of long-range transmembrane inter-helix residue contacts can significantly improve the quality of simulated membrane protein models. Results In this paper, we present an updated MemBrain predictor, which aims to predict transmembrane protein residue contacts. Our new model benefits from an efficient learning algorithm that can mine latent structural features, which exist in original feature space. The new MemBrain is a two-stage inter-helix contact predictor. The first stage takes sequence-based features as inputs and outputs coarse contact probabilities for each residue pair, which will be further fed into convolutional neural network together with predictions from three direct-coupling analysis approaches in the second stage. Experimental results on the training dataset show that our method achieves an average accuracy of 81.6% for the top L /5 predictions using a strict sequence-based jackknife cross-validation. Evaluated on the test dataset, MemBrain can achieve 79.4% prediction accuracy. Moreover, for the top L /5 predicted long-range loop contacts, the prediction performance can reach an accuracy of 56.4%. These results demonstrate that the new MemBrain is promising for transmembrane protein’s contact map prediction. Availability and implementation http://www.csbio.sjtu.edu.cn/bioinf/MemBrain/ Contact hbshen@sjtu.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation We recently published MS2LDA, a method for the decomposition of sets of molecular fragment data derived from large metabolomics experiments. To make the method more widely available to the community, here we present ms2lda.org, a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualizations. Results Ms2lda.org takes tandem mass spectrometry data in many standard formats and allows the user to infer the sets of fragment and neutral loss features that co-occur together (Mass2Motifs). As an alternative workflow, the user can also decompose a data set onto predefined Mass2Motifs. This is accomplished through the web interface or programmatically from our web service. Availability and implementation The website can be found at http://ms2lda.org , while the source code is available at https://github.com/sdrogers/ms2ldaviz under the MIT license. Contact simon.rogers@glasgow.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Linkage and quantitative trait loci (QTL) maps are critical tools for the study of the genetic basis of complex traits. With the advances in sequencing technology over the past decade, linkage map densities have been increasing dramatically, while the visualization tools have not kept pace. LinkageMapView is a free add-on package written in R that produces high resolution, publication-ready visualizations of linkage and QTL maps. While there is software available to generate linkage map graphics, none are freely available, produce publication quality figures, are open source and can run on all platforms. LinkageMapView can be integrated into map building pipelines as it seamlessly incorporates output from R/qtl and also accepts simple text or comma delimited files. There are numerous options within the package to build highly customizable maps, allow for linkage group comparisons, and annotate QTL regions. Availability and implementation https://cran.r-project.org/web/packages/LinkageMapView/ Contact louellet@uncc.edu

Description: Motivation Rapid and low cost sequencing of genomes enabled widespread use of genomic data in research studies and personalized customer applications, where genomic data is shared in public databases. Although the identities of the participants are anonymized in these databases, sensitive information about individuals can still be inferred. One such information is kinship. Results We define two routes kinship privacy can leak and propose a technique to protect kinship privacy against these risks while maximizing the utility of shared data. The method involves systematic identification of minimal portions of genomic data to mask as new participants are added to the database. Choosing the proper positions to hide is cast as an optimization problem in which the number of positions to mask is minimized subject to privacy constraints that ensure the familial relationships are not revealed. We evaluate the proposed technique on real genomic data. Results indicate that concurrent sharing of data pertaining to a parent and an offspring results in high risks of kinship privacy, whereas the sharing data from further relatives together is often safer. We also show arrival order of family members have a high impact on the level of privacy risks and on the utility of sharing data. Availability and implementation https://github.com/tastanlab/Kinship-Privacy Contact erman@cs.bilkent.edu.tr or oznur.tastan@cs.bilkent.edu.tr Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general purpose data mining tool exists for physicians, medical researchers and citizen scientists without significant training in programming and bioinformatics. To bridge this gap, we built the Omics AnalySIs System for PRecision Oncology (OASISPRO), a web-based system to mine the quantitative omics information from The Cancer Genome Atlas (TCGA). This system effectively visualizes patients’ clinical profiles, executes machine-learning algorithms of choice on the omics data and evaluates the prediction performance using held-out test sets. With this tool, we successfully identified genes strongly associated with tumor stage, and accurately predicted patients’ survival outcomes in many cancer types, including adrenocortical carcinoma. By identifying the links between omics and clinical phenotypes, this system will facilitate omics studies on precision cancer medicine and contribute to establishing personalized cancer treatment plans. Availability and implementation This web-based tool is available at http://tinyurl.com/oasispro ; source codes are available at http://tinyurl.com/oasisproSourceCode . Contact khyu@stanford.edu or mpsnyder@stanford.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Analysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret and experimentally validate. To address these challenges we developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex, non-binary, splicing variations. Using a matching primer design algorithm it also suggests to users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis. Availability and implementation Program and code will be available at http://majiq.biociphers.org/majiq-spel . Contact yosephb@upenn.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Accurate molecular structure of the protein dimer representing the elementary building block of intermediate filaments (IFs) is essential towards the understanding of the filament assembly, rationalizing their mechanical properties and explaining the effect of disease-related IF mutations. The dimer contains a ∼300-residue long α-helical coiled coil which cannot be assessed by either direct experimental structure determination or modelling using standard approaches. At the same time, coiled coils are well-represented in structural databases. Results Here we present CCFold, a generally applicable threading-based algorithm which produces coiled-coil models from protein sequence only. The algorithm is based on a statistical analysis of experimentally determined structures and can handle any hydrophobic repeat patterns in addition to the most common heptads. We demonstrate that CCFold outperforms general-purpose computational folding in terms of accuracy, while being faster by orders of magnitude. By combining the CCFold algorithm and Rosetta folding we generate representative dimer models for all IF protein classes. Availability and implementation The source code is freely available at https://github.com/biocryst/IF ; a web server to run the program is at http://pharm.kuleuven.be/Biocrystallography/cc . Contact sergei.strelkov@kuleuven.be Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Cells process information, in part, through transcription factor (TF) networks, which control the rates at which individual genes produce their products. A TF network map is a graph that indicates which TFs bind and directly regulate each gene. Previous work has described network mapping algorithms that rely exclusively on gene expression data and ‘integrative’ algorithms that exploit a wide range of data sources including chromatin immunoprecipitation sequencing (ChIP-seq) of many TFs, genome-wide chromatin marks, and binding specificities for many TFs determined in vitro . However, such resources are available only for a few major model systems and cannot be easily replicated for new organisms or cell types. Results We present NetProphet 2.0, a ‘data light’ algorithm for TF network mapping, and show that it is more accurate at identifying direct targets of TFs than other, similarly data light algorithms. In particular, it improves on the accuracy of NetProphet 1.0, which used only gene expression data, by exploiting three principles. First, combining multiple approaches to network mapping from expression data can improve accuracy relative to the constituent approaches. Second, TFs with similar DNA binding domains bind similar sets of target genes. Third, even a noisy, preliminary network map can be used to infer DNA binding specificities from promoter sequences and these inferred specificities can be used to further improve the accuracy of the network map. Availability and implementation Source code and comprehensive documentation are freely available at https://github.com/yiming-kang/NetProphet_2.0 . Contact brent@wustl.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on (i) summary statistics from genome-wide association studies (GWAS) and (ii) linkage disequilibrium patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our G ene-level J oint A nalysis of functional SNPs in C osmopolitan C ohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals. Results We propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses (i) cis-eQTL SNPs from the latest expression studies and (ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also (i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and (ii) to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q -values based on Holm adjustment of P -values. Availability and implementation https://github.com/Chatzinakos/JEPEGMIX2 . Contact chris.chatzinakos@vcuhealth.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem. Results We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. Availability and implementation The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top . Contact xuanping@hlju.edu.cn or zhang@hlju.edu.cn or lijzh@hit.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary The Quest for Orthologs (QfO) is an open collaboration framework for experts in comparative phylogenomics and related research areas who have an interest in highly accurate orthology predictions and their applications. We here report highlights and discussion points from the QfO meeting 2015 held in Barcelona. Achievements in recent years have established a basis to support developments for improved orthology prediction and to explore new approaches. Central to the QfO effort is proper benchmarking of methods and services, as well as design of standardized datasets and standardized formats to allow sharing and comparison of results. Simultaneously, analysis pipelines have been improved, evaluated and adapted to handle large datasets. All this would not have occurred without the long-term collaboration of Consortium members. Meeting regularly to review and coordinate complementary activities from a broad spectrum of innovative researchers clearly benefits the community. Highlights of the meeting include addressing sources of and legitimacy of disagreements between orthology calls, the context dependency of orthology definitions, special challenges encountered when analyzing very anciently rooted orthologies, orthology in the light of whole-genome duplications, and the concept of orthologous versus paralogous relationships at different levels, including domain-level orthology. Furthermore, particular needs for different applications (e.g. plant genomics, ancient gene families and others) and the infrastructure for making orthology inferences available (e.g. interfaces with model organism databases) were discussed, with several ongoing efforts that are expected to be reported on during the upcoming 2017 QfO meeting. Contact selewis@lbl.gov or c.dessimoz@ucl.ac.uk

Description: Motivation In recent years, the massively parallel cDNA sequencing (RNA-Seq) technologies have become a powerful tool to provide high resolution measurement of expression and high sensitivity in detecting low abundance transcripts. However, RNA-seq data requires a huge amount of computational efforts. The very fundamental and critical step is to align each sequence fragment against the reference genome. Various de novo spliced RNA aligners have been developed in recent years. Though these aligners can handle spliced alignment and detect splice junctions, some challenges still remain to be solved. With the advances in sequencing technologies and the ongoing collection of sequencing data in the ENCODE project, more efficient alignment algorithms are highly demanded. Most read mappers follow the conventional seed-and-extend strategy to deal with inexact matches for sequence alignment. However, the extension is much more time consuming than the seeding step. Results We proposed a novel RNA-seq de novo mapping algorithm, call DART, which adopts a partitioning strategy to avoid the extension step. The experiment results on synthetic datasets and real NGS datasets showed that DART is a highly efficient aligner that yields the highest or comparable sensitivity and accuracy compared to most state-of-the-art aligners, and more importantly, it spends the least amount of time among the selected aligners. Availability and implementation https://github.com/hsinnan75/DART Contact hsu@iis.sinica.edu.tw Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Gene expression alterations and potentially underlying gene copy number mutations can be measured routinely in the wet lab, but it is still extremely challenging to quantify impacts of altered genes on clinically relevant characteristics to predict putative driver genes. We developed the R package regNet that utilizes gene expression and copy number data to learn regulatory networks for the quantification of potential impacts of individual gene expression alterations on user-defined target genes via network propagation. We demonstrate the value of regNet by identifying putative major regulators that distinguish pilocytic from diffuse astrocytomas and by predicting putative impacts of glioblastoma-specific gene copy number alterations on cell cycle pathway genes and patient survival. Availability and implementation regNet is available for download at https://github.com/seifemi/regNet under GNU GPL-3 . Contact michael.seifert@tu-dresden.de Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information. Results In this study, we propose a new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases, especially for new diseases and miRNAs or those diseases and miRNAs with sparse known associations. First, we integrate the disease semantic information and miRNA functional information to estimate disease similarity and miRNA similarity, respectively. Considering that there is no available interaction observed for new diseases or miRNAs, a preprocessing step is developed to construct the interaction score profiles that will assist in prediction. Next, a graph regularized non-negative matrix factorization framework is utilized to simultaneously identify potential associations for all diseases. The results indicated that our proposed method can effectively prioritize disease-associated miRNAs with higher accuracy compared with other recent approaches. Moreover, case studies also demonstrated the effectiveness of GRNMF to infer unknown miRNA-disease associations for those novel diseases and miRNAs. Availability and implementation The code of GRNMF is freely available at https://github.com/XIAO-HN/GRNMF/ . Contact luojiawei@hnu.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation A deleterious amino acid change in a protein can be compensated by a second-site rescue mutation. These compensatory mechanisms can be mimicked by drugs. In particular, the location of rescue mutations can be used to identify protein regions that can be targeted by small molecules to reactivate a damaged mutant. Results We present the first general computational method to detect rescue sites. By mimicking the effect of mutations through the application of forces, the double force scanning (DFS) method identifies the second-site residues that make the protein structure most resilient to the effect of pathogenic mutations. We tested DFS predictions against two datasets containing experimentally validated and putative evolutionary-related rescue sites. A remarkably good agreement was found between predictions and experimental data. Indeed, almost half of the rescue sites in p53 was correctly predicted by DFS, with 65% of remaining sites in contact with DFS predictions. Similar results were found for other proteins in the evolutionary dataset. Availability and implementation The DFS code is available under GPL at https://fornililab.github.io/dfs/ Contact m.tiberti@qmul.ac.uk or a.fornili@qmul.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: A transient three-dimensional thermal analysis using finite element method was conducted to determine the effect of varied convection applied on the substrate on the thermal history of a multi-layer/multi-pass (per layer) build in direct laser deposition (DLD) of SS316 steel. The effect of the coefficient of heat convection was studied on thermal cycling, thermal gradient, and the output net heat during the deposition process. The results showed that the convection coefficient on the bottom surface of the substrate has negligible effects on the thermal history of the build, thus proving that this parameter may not be used to control the thermal history, and consequently, the microstructure of the build. The results also showed 12% increase in the dissipated heat from the build to the substrate when the coefficient of heat convection changed from 0 to 10,000 W/m 2  K. In comparison to the current knowledge, modeling a multi-layer/multi-pass (per layer) geometry provided a better understanding of the thermal history of the build during the DLD process.

Description: Clinch joining is a complexed mechanical behavior influenced by both process parameters and geometry parameters. The present clinch joining optimizations are more dedicated on geometry parameters, and except some property objectives of optimization, the other design properties of clinching process are not considered as constrained conditions. Therefore, the multiobjective optimization in the paper takes clinching process parameters such as punch speed, bottom thickness, and blank holder force into consideration besides geometry parameters, and then sets two thinning rates as constrained conditions. In the paper, a sufficient and systemic procedure for high-quality clinched joint based on FEA (finite element analysis), PS (parameter study), RSM (response surface methodology), and NSGA-II (non-dominated sorting genetic algorithm-II) is developed. Parameter study is conducted to choose significant parameters from both process and geometric parameters for neck, interlock, and tensile force. Then, mathematical models of nine selected parameters with responses are built based on RSM, and the interactions on interlock and tensile force are clearly identified by the model. Finally, three objectives neck, interlock, and tensile force and two constraints thinning rates of side face of joint are applied on multiobjective optimization using NSGA-II, which could obtain comprehensive high-quality joint, and the Pareto sets reveal negative relations of both tensile force to neck and interlock to neck, as well as the positive relation between interlock and tensile force. The result shows that the presented procedure performs well in optimization of clinch joining process and the use of finite element simulation model is valuable for complex optimization design.

Description: Residual stress on the machined surface and the subsurface is known to influence the distortion of thin-walled parts. Therefore, it is essential to predict the distribution of surface residual stress accurately. In this paper, the coupled distribution law of initial residual stress and machining-induced residual stress is investigated. Firstly, a model with initial residual stress is established and incorporated into thermal mechanical coupled finite element model of 2-D cutting. Then, a tensile fixture is designed to impose initial stress into a thin-walled part of Al-6061, and cutting experiments are carried out. The residual stress distribution is measured by X-ray diffraction/electropolishing method. The results of experiments and simulation show that in the plastic deformation zone, the initial residual stress has no significant influence on the distribution of the machining-induced residual stress. In the elastic deformation zone, the stress that linear grows along depth from zero to initial residual stress is superimposed on machining-induced residual stress. The mathematical model of stress coupling distribution on the surface of thin-walled parts is established by numerical method. Finally, it is found that the effect of coupled stress distribution on distortion is more significant with the decrease of thickness (from 3 to 0.5 mm) of the thin-walled parts.

Description: An improved full-discretization method (IFDM) based on the golden search is presented in this brief paper to predict stability lobe diagram (SLD). To begin with, the mathematical model of milling dynamics considering the regenerative chatter is expressed as a state space form. With the time delay being separated equally into a limited amount of elements, the time series expression is obtained by interpolating the integral nonhomogeneous term using linear approximation. Then, 2 N order algorithm is adopted to resolve the exponential term into a real matrix, which avoids the exponential matrix that has to be calculated each time in scanning the plane comprised of axial cutting depth and spindle speed. Lastly, the golden search instead of traditional sequential search is applied to seek the crucial axial cutting depths corresponding to different spindle speeds, which can improve computational efficiency remarkably. The verifications with two classic benchmark examples demonstrate that the proposed method has higher computational efficiency.

Description: Regular precision-shaped abrasive grains are preferred to irregular grits due to their superior performance regarding uniform polishing. Three-dimensional modelling of abrasive disc’s topography is essential to understand the material removal rate and surface roughness estimations through finite element based numerical simulations. Topography modelling of one such precision (prism)-shaped grain abrasive disc is carried out in this work through stochastic studies. The abrasive discs are scanned using laser profilometer, and high-frequency noise is filtered out using spectral analysis. Spatial parameters such as autocorrelation length and texture aspect ratio are considered for the topology mapping. Based on the statistical information from the measured grit sizes #60 and #120, such as peak protrusion grain height, spatial distributions, the topography is simulated. The analysis reveals that the peak height and spatial distribution follow a normal distribution. Unlike irregularly shaped grains where the orientations of the grains are neglected, the height variations in the precision-shaped grains in coated abrasive discs are mainly caused by random orientations of the grains. So, iterations are carried out for orientation in the three (mutually perpendicular) axes of the grain till the required statistical parameters are achieved. Surface fitting is performed for the distributed grains and the 3D-surface parameters of the simulated coated abrasive topography match well with the actual discs.

Description: The serrated chip is a remarkable characteristic in the machining of the Ti-6Al-4 V alloy, but its formation mechanism is not completely understood. Finite element modeling is an effective method for studying the complex thermo-mechanical problems that occur during the machining process. The constitutive model plays a key role in the simulation, and Johnson-Cook (JC) model is the most commonly used model. However, the JC model neglects the coupled effects of the strain, strain rate, and temperature, and the material is assumed to be homogeneous. Thus, the JC model is applicable for studying macroscale machining. In fact, cutting is a complex thermo-mechanical process, and there are material defects in the Ti-6Al-4 V alloy. First, a modified JC model is proposed based on the THAN law, which is derived from material defects considering the coupled effects of the strain, strain rate, temperature, and strain softening. Then, the serrated chip morphology and cutting force predicted by the JC-THAN model are compared with experimental data. Finally, the serrated chip formation mechanism from macroscale machining to microscale machining is analyzed considering the material defects based on the JC-THAN model, and the dominated formation mechanism at each scale is found.

Description: Opportunities for new applications of lightweight Mg alloys are emerging in bio-medical fields, especially in medical devices and implants. The biodegradable Mg-based alloys wield advantages over their counterparts of SS 316 L, Co-Cr, and Ti-based alloys due to good biocompatibility observed during in-vivo and in-vitro assessments. However, in such aqueous environments, biodegradable magnesium alloys experience the limitation of higher corrosion rates, which causes loosening of the fixation implant. More importantly, it causes an undesirable chemical imbalance in the human body. This undesirable side effect of the promising Mg alloys can be subdued by improving the quality of the implant’s surface which interacts with the human biology. One way to do it is by imparting compressive stress via mechanical processing such as tool-based machining process. Mechanical micro-machining, especially the ultra-precision turning, could be a viable method to fabricate Mg alloy implants and components with the high surface finish necessary for superior corrosion resistance. However, the cutting mechanics of Mg alloy is poorly understood due to the scarcity of processing data and cutting parameters at ultra-precision level. In this paper, with ultra-precise cutting of Mg alloy (AZ91D), a novel “burnishing-like” surface finishing phenomena has been established. Additionally, some of the critical machining results are identified that are crucial for the comprehension of the cutting mechanics. These entities are cutting edge radius effect on the chip formation, material flow angle, machining-induced stress, surface burn marks, chip-tool contact surface, and the fallacy of “no chip formation” phenomenon. Therefore, knowledge derived from this study will enhance the understanding of cutting mechanics at ultra-precision level, and consequently improve the machining results of Mg alloy for bio-medical, electro-mechanical, and space-telecommunication applications.

Description: Surface nanomachining based on single-point diamond turning is a viable technology for the preparation of high-quality surfaces. It found its first extensive use in infrared optics, but it is also attractive for X-ray optics. The challenge is that the X-rays used in laboratories have wavelengths 4 orders of magnitude shorter than infrared radiation. Though recent studies have shown promising results, periodic surface ripples as a footprint of the regular cutting tool scanning proved to be an unavoidable feature, producing unwanted effects in reciprocal space. Hence, the presented paper addresses the problem of finishing Ge monocrystalline X-ray surfaces while keeping all benefits of nanomachining. To do so, chemomechanical polishing, Ar + ion beam bombardment, and UV laser irradiation were tested separately. Chemomechanical polishing proved to be rather time consuming, whereas ion beam bombardment was not sufficient to suppress the ripples even at the highest available energies. In contrast, UV laser irradiation turned out to be a suitable and time-efficient finishing tool to fully suppress the ripples while keeping the local rms surface roughness around 1 nm.

Description: Summary Refinement of protein structure models is a long-standing problem in structural bioinformatics. Molecular dynamics-based methods have emerged as an avenue to achieve consistent refinement. The PREFMD web server implements an optimized protocol based on the method successfully tested in CASP11. Validation with recent CASP refinement targets shows consistent and more significant improvement in global structure accuracy over other state-of-the-art servers. Availability and implementation PREFMD is freely available as a web server at http://feiglab.org/prefmd . Scripts for running PREFMD as a stand-alone package are available at https://github.com/feiglab/prefmd.git . Contact feig@msu.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers. Availability and implementation https://github.com/chengjun583/KIRP-topological-features Contact 1271992826@qq.com or kunhuang@iu.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins. Results We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker’s ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology. Availability and implementation The CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/. Contact deane@stats.ox.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary We developed a prokaryotic genome annotation pipeline, DFAST, that also supports genome submission to public sequence databases. DFAST was originally started as an on-line annotation server, and to date, over 7000 jobs have been processed since its first launch in 2016. Here, we present a newly implemented background annotation engine for DFAST, which is also available as a standalone command-line program. The new engine can annotate a typical-sized bacterial genome within 10 min, with rich information such as pseudogenes, translation exceptions and orthologous gene assignment between given reference genomes. In addition, the modular framework of DFAST allows users to customize the annotation workflow easily and will also facilitate extensions for new functions and incorporation of new tools in the future. Availability and implementation The software is implemented in Python 3 and runs in both Python 2.7 and 3.4—on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast_core/under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/ . Contact yn@nig.ac.jp Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Biodiversity databases now comprise hundreds of thousands of sequences and trait records. For example, the Open Tree of Life includes over 1 491 000 metazoan and over 300 000 bacterial taxa. These data provide unique opportunities for analysis of phylogenetic trait distribution and reconstruction of ancestral biodiversity. However, existing tools for comparative phylogenetics scale poorly to such large trees, to the point of being almost unusable. Results Here we present a new R package, named ‘castor’, for comparative phylogenetics on large trees comprising millions of tips. On large trees castor is often 100–1000 times faster than existing tools. Availability and implementation The castor source code, compiled binaries, documentation and usage examples are freely available at the Comprehensive R Archive Network (CRAN). Contact louca.research@gmail.com Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian. Results We provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth. Availability and implementation Software is available via the open source image analysis system TINA Vision, www.tina-vision.net . Contact paul.tar@manchester.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation VizGVar was designed to meet the growing need of the research community for improved genomic and proteomic data viewers that benefit from better information visualization. Results We implemented a new information architecture and applied user centered design principles to provide a new improved way of visualizing genetic information and protein data related to human disease. VizGVar connects the entire database of Ensembl protein motifs, domains, genes and exons with annotated SNPs and somatic variations from PharmGKB and COSMIC . VizGVar precisely represents genetic variations and their respective location by colored curves to designate different types of variations. The structured hierarchy of biological data is reflected in aggregated patterns through different levels, integrating several layers of information at once. VizGVar provides a new interactive, web-based JavaScript visualization of somatic mutations and protein variation, enabling fast and easy discovery of clinically relevant variation patterns. Availability and implementation VizGVar is accessible at http://vizport.io/vizgvar ; http://vizport.io/vizgvar/doc/ . Contact asolano@broadinstitute.org or allan.orozcosolano@ucr.ac.cr

Description: Motivation RNA structural motif finding is a relevant problem that becomes computationally hard when working on high-throughput data (e.g. eCLIP, PAR-CLIP), often represented by thousands of RNA molecules. Currently, the BEAM server is the only web tool capable to handle tens of thousands of RNA in input with a motif discovery procedure that is only limited by the current secondary structure prediction accuracies. Results The recently developed method BEAM (BEAr Motifs finder) can analyze tens of thousands of RNA molecules and identify RNA secondary structure motifs associated to a measure of their statistical significance. BEAM is extremely fast thanks to the BEAR encoding that transforms each RNA secondary structure in a string of characters. BEAM also exploits the evolutionary knowledge contained in a substitution matrix of secondary structure elements, extracted from the RFAM database of families of homologous RNAs. The BEAM web server has been designed to streamline data pre-processing by automatically handling folding and encoding of RNA sequences, giving users a choice for the preferred folding program. The server provides an intuitive and informative results page with the list of secondary structure motifs identified, the logo of each motif, its significance, graphic representation and information about its position in the RNA molecules sharing it. Availability and implementation The web server is freely available at http://beam.uniroma2.it/ and it is implemented in NodeJS and Python with all major browsers supported. Contact marco.pietrosanto@uniroma2.it Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation A significant difference in the distribution of a feature between two gene sets can provide insight into function or regulation. This statistical setting differs from much of hypothesis testing theory because the genome is often considered to be effectively fixed, finite and entirely known in commonly studied organisms, such as human. The Mann–Whitney U test is commonly employed in this scenario despite the assumptions of the test not being met, leading to unreliable and generally underpowered results. Permutation tests are also commonly employed for this purpose, but are computationally burdensome and are not tractable for obtaining small P values or for multiple comparisons. Results We present an exact test for the null hypothesis that gene set membership is independent of the quantitative gene feature of interest. We derive an analytic expression for the randomization distribution of the median of the quantitative feature under the null hypothesis. Efficient implementation permits calculation of precise P values of arbitrary magnitude and makes thousands of simultaneous tests of transcriptome-sized gene sets computationally tractable. The flexibility of the hypothesis testing framework presented permits extension to a variety of related tests commonly found in genomics. The exact test is used to identify signatures of translation control and protein function in the human genome. Availability and implementation The exact test presented here is implemented in R in the package kpmt available on CRAN. Contact map2085@med.cornell.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Dynamic assessment of microbial ecology (DAME) is a Shiny-based web application for interactive analysis and visualization of microbial sequencing data. DAME provides researchers not familiar with R programming the ability to access the most current R functions utilized for ecology and gene sequencing data analyses. Currently, DAME supports group comparisons of several ecological estimates of α-diversity and β-diversity, along with differential abundance analysis of individual taxa. Using the Shiny framework, the user has complete control of all aspects of the data analysis, including sample/experimental group selection and filtering, estimate selection, statistical methods and visualization parameters. Furthermore, graphical and tabular outputs are supported by R packages using D3.js and are fully interactive. Availability and implementation DAME was implemented in R but can be modified by Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. It is freely available on the web at https://acnc-shinyapps.shinyapps.io/DAME/ . Local installation and source code are available through Github ( https://github.com/bdpiccolo/ACNC-DAME ). Any system with R can launch DAME locally provided the shiny package is installed. Contact bdpiccolo@uams.edu

Description: Motivation Chromatin immunoprecipitation sequencing (ChIP-seq) experiments are inexpensive and time-efficient, and result in massive datasets that introduce significant storage and maintenance challenges. To address the resulting Big Data problems, we propose a lossless and lossy compression framework specifically designed for ChIP-seq Wig data, termed ChIPWig. ChIPWig enables random access, summary statistics lookups and it is based on the asymptotic theory of optimal point density design for nonuniform quantizers. Results We tested the ChIPWig compressor on 10 ChIP-seq datasets generated by the ENCODE consortium. On average, lossless ChIPWig reduced the file sizes to merely 6% of the original, and offered 6-fold compression rate improvement compared to bigWig. The lossy feature further reduced file sizes 2-fold compared to the lossless mode, with little or no effects on peak calling and motif discovery using specialized NarrowPeaks methods. The compression and decompression speed rates are of the order of 0.2 sec/MB using general purpose computers. Availability and implementation The source code and binaries are freely available for download at https://github.com/vidarmehr/ChIPWig-v2 , implemented in C ++. Contact milenkov@illinois.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. Results We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Availability and implementation Proposed methods are available in the R package mixKernel , released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/ . Contact jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Large-scale molecular data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, standard approaches for omics data analysis ignore the group structure among genes encoded in functional relationships or pathway information. Results We propose new Bayesian hierarchical generalized linear models, called group spike-and-slab lasso GLMs, for predicting disease outcomes and detecting associated genes by incorporating large-scale molecular data and group structures. The proposed model employs a mixture double-exponential prior for coefficients that induces self-adaptive shrinkage amount on different coefficients. The group information is incorporated into the model by setting group-specific parameters. We have developed a fast and stable deterministic algorithm to fit the proposed hierarchal GLMs, which can perform variable selection within groups. We assess the performance of the proposed method on several simulated scenarios, by varying the overlap among groups, group size, number of non-null groups, and the correlation within group. Compared with existing methods, the proposed method provides not only more accurate estimates of the parameters but also better prediction. We further demonstrate the application of the proposed procedure on three cancer datasets by utilizing pathway structures of genes. Our results show that the proposed method generates powerful models for predicting disease outcomes and detecting associated genes. Availability and implementation The methods have been implemented in a freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ ). Contact nyi@uab.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Gene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to noise and frequently do not replicate in external validation sets. For complex, heterogeneous diseases, these classifiers are further limited by being unable to capture varying combinations of genes that lead to the same phenotype. Pathway-based classification can overcome these challenges by using robust, aggregate features to represent biological mechanisms. In this work, we developed a novel pathway-based approach, PRObabilistic Pathway Score, which uses genes to calculate individualized pathway scores for classification. Unlike previous individualized pathway-based classification methods that use gene sets, we incorporate gene interactions using probabilistic graphical models to more accurately represent the underlying biology and achieve better performance. We apply our method to differentiate two similar complex diseases, ulcerative colitis (UC) and Crohn’s disease (CD), which are the two main types of inflammatory bowel disease (IBD). Using five IBD datasets, we compare our method against four gene-based and four alternative pathway-based classifiers in distinguishing CD from UC. We demonstrate superior classification performance and provide biological insight into the top pathways separating CD from UC. Availability and Implementation PROPS is available as a R package, which can be downloaded at http://simtk.org/home/props or on Bioconductor . Contact rbaltman@stanford.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation The branchpoint element is required for the first lariat-forming reaction in splicing. However current catalogues of human branchpoints remain incomplete due to the difficulty in experimentally identifying these splicing elements. To address this limitation, we have developed a machine-learning algorithm—branchpointer—to identify branchpoint elements solely from gene annotations and genomic sequence. Results Using branchpointer, we annotate branchpoint elements in 85% of human gene introns with sensitivity (61.8%) and specificity (97.8%). In addition to annotation, branchpointer can evaluate the impact of SNPs on branchpoint architecture to inform functional interpretation of genetic variants. Branchpointer identifies all published deleterious branchpoint mutations annotated in clinical variant databases, and finds thousands of additional clinical and common genetic variants with similar predicted effects. This genome-wide annotation of branchpoints provides a reference for the genetic analysis of splicing, and the interpretation of noncoding variation. Availability and implementation Branchpointer is written and implemented in the statistical programming language R and is freely available under a BSD license as a package through Bioconductor. Contact b.signal@garvan.org.au or t.mercer@garvan.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Alternative splicing is a biological process of fundamental importance in most eukaryotes. It plays a pivotal role in cell differentiation and gene regulation and has been associated with a number of different diseases. The widespread availability of RNA-Sequencing capacities allows an ever closer investigation of differentially expressed isoforms. However, most tools for differential alternative splicing (DAS) analysis do not take split reads, i.e. the most direct evidence for a splice event, into account. Here, we present DIEGO, a compositional data analysis method able to detect DAS between two sets of RNA-Seq samples based on split reads. Results The python tool DIEGO works without isoform annotations and is fast enough to analyze large experiments while being robust and accurate. We provide python and perl parsers for common formats. Availability and implementation The software is available at: www.bioinf.uni-leipzig.de/Software/DIEGO . Contact steve@bioinf.uni-leipzig.de Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation The collection, management and visualization of clinical pedigree (family history) data is a core activity in clinical genetics centres. However, clinical pedigree datasets can be difficult to manage, as they are time consuming to capture, and can be difficult to build, manipulate and visualize graphically. Several standalone graphical pedigree editors and drawing applications exist but there are no freely available lightweight graphical pedigree editors that can be easily configured and incorporated into web applications. Results We developed ‘pedigreejs’, an interactive graphical pedigree editor written in JavaScript, which uses standard pedigree nomenclature. Pedigreejs provides an easily configurable, extensible and lightweight pedigree editor. It makes use of an open-source Javascript library to define a hierarchical layout and to produce images in scalable vector graphics (SVG) format that can be viewed and edited in web browsers. Availability and implementation The software is freely available under GPL licence ( https://ccge-boadicea.github.io/pedigreejs/ ). Contact tjc29@cam.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Molecular profiling techniques have evolved to single-cell assays, where dense molecular profiles are screened simultaneously for each cell in a population. High-throughput single-cell experiments from a heterogeneous population of cells can be experimentally and computationally sorted as a sequence of samples pseudo-temporally ordered samples. The analysis of these datasets, comprising a large number of samples, has the potential to uncover the dynamics of the underlying regulatory programmes. Results We present a novel approach for modelling and inferring gene regulatory networks from high-throughput time series and pseudo-temporally sorted single-cell data. Our method is based on a first-order autoregressive moving-average model and it infers the gene regulatory network within a variational Bayesian framework. We validate our method with synthetic data and we apply it to single cell qPCR and RNA-Seq data for mouse embryonic cells and hematopoietic cells in zebra fish. Availability and implementation The method presented in this article is available at https://github.com/mscastillo/GRNVBEM . Contact mscastillo@ugr.es

Description: Motivation Carbohydrates play crucial roles in various biochemical processes and are useful for developing drugs and vaccines. However, in case of carbohydrates, the primary structure elucidation is usually a sophisticated task. Therefore, they remain the least structurally characterized class of biomolecules, and it hampers the progress in glycochemistry and glycobiology. Creating a usable instrument designed to assist researchers in natural carbohydrate structure determination would advance glycochemistry in biomedical and pharmaceutical applications. Results We present GRASS (Generation, Ranking and Assignment of Saccharide Structures), a novel method for semi-automated elucidation of carbohydrate and derivative structures which uses unassigned 13 C NMR spectra and information obtained from chromatography, optical, chemical and other methods. This approach is based on new methods of carbohydrate NMR simulation recently reported as the most accurate. It combines a broad diversity of supported structural features, high accuracy and performance. Availability and implementation GRASS is implemented in a free web tool available at http://csdb.glycoscience.ru/grass.html . Contact kapaev_roman@mail.ru or netbox@toukach.ru Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Micro manufacturing using micro direct metal deposition was successfully implemented on NiTi powder alloy. Micro direct metal deposition was optimized through a set of process parameters and designed experiments to improve the geometrical accuracy and repeatability of micro fabrication. Scanning electron microscopy and micro X-ray computed tomography were used to analyze the surface quality, micro porosity, and deviations of products with respect to nominal geometrical models. Results showed that increasing scanning speed is an efficient way to improve the surface quality and decrease the micro porosity content. Below 15% deviation to nominal geometrical models was achieved in hollow NiTi samples through a set of micro direct metal deposition process parameters and designed experiments.

Description: In this paper, a 3D finite element model for simulating the 3-mm-diameter peck drilling behavior of SiCp/Al composites was developed by using commercial code ABAQUS/Explicit. In order to improve the calculation accuracy, the proposed model incorporates a real drill by reverse engineering, a Johnson-Cook model developed for SiCp/Al composites, the damage initiation for chip separation, and characteristic-length related damage evolution for representing strain-softening behavior after damage initiation. The FE model is verified with the corresponding small-hole wet peck drilling experiments in terms of thrust force and torque. Then, the model is used to study on the influence of edge radius on the chip formation. The predicted chip is divided into two segments due to the sharp intersection of two curved edges of lips, and this is in good consistency with the actual chips from the experiment. At last, the formation of edge defects is analyzed by a modified FE model. The actual edge defect formation is determined by the random distribution of SiC particles and complicated crack types and opening theory. This necessitates the establishment of microstructure-based model in the future.

Description: This paper proposes a novel and coherent analysis of mass transfer and thermal stress flow (direction) in solidification of spherical components such as acetabular shells in selective laser melting of Ti-6Al-4 V. A detailed description of different phenomena such as solidification behaviour at the solid/liquid interface, behaviour of moving interface, thermal gradient, Gibbs free energy, interfacial energy and equilibrium temperature were utilised to characterise mass and thermal stress flow. Experimental results showed that thermal stress and mass flow on outer surfaces are higher than those found on inner surfaces which are related to the geometry and shape of the printed structures. Due to the mass transfer and thermal stress flow, built-up lines including a dent in the outer surfaces and grooves in the inner surfaces were observed with heights on the order of 200 μm. Post-processing is needed to remove the built-up lines which results in increasing dimensional deviations.

Description: Finite Element (FE) modeling of machining processes has received growing attention over the last two decades. Since machining processes operate at severe deformation conditions, involving very high strain, strain rate, stress, and temperature, the modeling procedure is still a challenging task even with new advanced software and computers. Therefore, most of the published research works were mainly performed for the simplest configuration of machining known as orthogonal cutting, in which a plane strain deformation state is assumed. This configuration leads to reducing the number of elements in the FE model and the computational time of the solution, compared to conventional machining processes (turning, milling, and drilling). Nevertheless, FE modeling of orthogonal turning is still considered as an open-ended subject as most of the phenomena involved in the orthogonal turning process, which also exist in other machining operations, are not fully well understood. The present review article deals with finite element modeling of orthogonal machining process. The paper consists of several related parts. First, the fundamentals of the FE simulation of orthogonal turning process are briefly described. Then, a detailed review on the FE prediction of machining characteristics including chip morphology, cutting forces, cutting temperature, tool wear, and burr formation is provided. Also, the FE prediction of surface integrity characteristics including residual stresses and microstructural changes is discussed. The influence of input model and parameters including thermal, material, and frictional models as well as size and arrangement of elements on machining and surface integrity characteristics are explained as well. Both technical and statistical aspects of the FE simulation of orthogonal turning are treated. Since there is no review paper thoroughly and specifically discussing the methods and findings of the finite element simulation of turning operations, the present article can be used as a reference for researchers who are active and/or interested in this filed.

Description: In mechanical product design, rapid and effective simulation is invariably essential since the design flaws can be spotted by the simulation results in advance. However, currently, most designers always perform assembly process and kinematics simulation separately, which may lead to repetitive work and low design efficiency. To address the problem, this paper presents a novel method for product design which aims to integrate the assembly process and kinematics simulation seamlessly. To begin with, a semantic modeling method for part information is introduced, which is the premise and foundation to achieve the integration. Secondly, the assembly process simulation is carried out according to the defined steps derived from interactive semantic reasoning among parts. Moreover, the completed assembled model is utilized to generate kinematic pairs automatically, which can help avoid a lot of unnecessary preparations for kinematics simulation. Meanwhile, physics engine is adopted to provide a stable kinematics operation. Based on these, the assembly plan and motion characteristics of a new product can be analyzed effectively and quickly. Therefore, the designers can obtain the analytical results and attempt different design schemes readily. Finally, the effectiveness of the integrated simulation method is verified by a case study of excavator assembly process and kinematics simulation.

Description: This paper addressed a fabricating method for spherical lens array mold by microforging. The silicon nitride ceramic balls were performed as indenter on metal materials, such as aluminum alloy, to generate dimples with spherical cross-sectional profiles. Under the proper processing conditions, the spherical lens array mold was fabricated. The effects of indentation force, depth, and pressing time were experimentally investigated. In addition, the morphology and surface property of microforged mold were inspected. Subsequently, the thermoplastic polymer spherical lens array was formed through hot embossing process. The optical performance, such as light spots and focal lengths, was detected. This technique shows great potential for mass production of spherical lens array mold.

Description: Laser-accelerated metallic flyers can be used in microscale manufacturing processes by providing high-speed impact and shockwave to target materials. This study conducted microscale punching experiments using a laser-accelerated flyer for various mold sizes, workpiece thicknesses, and flyer thicknesses to investigate performance and failure characteristics of the punching process. In the case of a single flyer impact, punching was successful when the ratio of mold diameter to workpiece thickness exceeded 3. High-speed punching advantages, which improve punching quality as the shear zone increases and burrs become smaller, were validated. When flyer thickness was significantly reduced, punching performance deteriorated although higher speed could be realized, because the thinner flyer was difficult to maintain flatness during acceleration, resulting in inefficient forming pressure transfer to the workpiece. When the ratio of mold diameter to workpiece thickness was less than 3, punching was achieved through multiple flyer impacts. However, pressure transfer efficiency from the flyer to the workpiece was further reduced. Thus, punching performance could be improved by reducing the relative size of the flyer to the mold diameter.

Description: Linear friction welding is a solid-state process that comprises rapid heating and cooling of the welded parts. Residual stresses (RS) as in every other welding process cannot be avoided. The presence of RS compromises the in-service performance and reliability. They influence stress corrosion cracking, fatigue strength, and the crack growth rate. Knowing the magnitude and nature of such stresses is critical for improving the quality of welded joints. Therefore, four different manufacturing stages of linear friction welded chain links were analyzed in the present study: “as forged” ( F ), “as welded” ( A ), “as welded” without flash ( N ), and post weld heat treated ( P ). The residual stress field was measured using the hole drilling (HD) method. The results of the hole drilling method showed to be independent of the measured position and symmetry with respect to the weld was observed in all evaluated regions. Close to the weld center line (WCL), the compressive stresses present in the “as forged” condition switched to tensile as a result of the welding process. However, in further regions, stresses remained almost unchanged for either A and N . The PWHT uniformizes the residual stress field along the whole weld region.

Description: Nano-hydroxyapatite (nHA) based polymer biocomposites have been widely used in biomedical industry because of biocompatibility and improved mechanical properties. The goal of this study was to produce ultra high molecular weight (UHMW) polyethylene/nHA composite via heat-assisted friction stir processing and to investigate the effects of process parameters on composite’s microstructure and tensile characteristics. The process parameters include rotational speed ( ω ), feed rate ( f ), volume fraction of nHA ( v ), and shoulder temperature ( T ). ANOVA was performed to identify the significant process parameters. Moreover, empirical models for tensile properties were developed and validated. Microscopic analysis revealed that the process parameters substantially affect the dispersion of reinforcing particles, formation of defects, and mixing of materials. Low levels of ω and T and high levels of f and v lead to defects (agglomeration of reinforcing particles, voids, and channels). Five percent volume of nHA with medium levels of other process parameters resulted in better material mixing, minimum defects, and better mechanical properties (10% increase in strength with only 1% reduction in percent elongation, relative to the parent material).

Description: The purpose of this research is to develop a computer-driven decision support system (DSS) to select optimal additive manufacturing (AM) machines for metal powder bed fusion (PBF) applications. The tool permits to evaluate productivity factors (i.e., cost and production time) for any given geometry. At the same time, the trade-off between feature resolution and productivity analysis is visualized and a sensitivity analysis is performed to evaluate future cost developments. This research encompasses a decision support system that includes a data structure and an algorithm which is coded in “MathWorks Matlab,” considering cost structures for metal-based AM (i.e., machine cost, material cost, and labor cost). Results of this research demonstrate that feature resolution has a crucial effect on the total cost per part, but displays decreasing impacts for higher build volume rates. Based on assumptions of business consultancies, productivity can be increased, resulting in a potential decline of cost per part of up to 55% until 2025. Using this DSS tool, it is possible to evaluate the most optimal AM production systems by selecting between several input parameters. The algorithm allows industry practitioners to retrieve information and assist in decision-making processes, including cost per part, total cost comparison, and build time evaluations for typical commercial metal PBF systems.

Description: Motivation Planarians are emerging as a model organism to study regeneration in animals. However, the little available data of protein–protein interactions hinders the advances in understanding the mechanisms underlying its regenerating capabilities. Results We have developed a protocol to predict protein–protein interactions using sequence homology data and a reference Human interactome. This methodology was applied on 11 Schmidtea mediterranea transcriptomic sequence datasets. Then, using Neo4j as our database manager, we developed PlanNET, a web application to explore the multiplicity of networks and the associated sequence annotations. By mapping RNA-seq expression experiments onto the predicted networks, and allowing a transcript-centric exploration of the planarian interactome, we provide researchers with a useful tool to analyse possible pathways and to design new experiments, as well as a reproducible methodology to predict, store, and explore protein interaction networks for non-model organisms. Availability and implementation The web application PlanNET is available at https://compgen.bio.ub.edu/PlanNET . The source code used is available at https://compgen.bio.ub.edu/PlanNET/downloads . Contact jabril@ub.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Split-networks are a generalization of phylogenetic trees that have proven to be a powerful tool in phylogenetics. Various ways have been developed for computing such networks, including split-decomposition, NeighborNet, QNet and FlatNJ. Some of these approaches are implemented in the user-friendly SplitsTree software package. However, to give the user the option to adjust and extend these approaches and to facilitate their integration into analysis pipelines, there is a need for robust, open-source implementations of associated data structures and algorithms. Here, we present SPECTRE, a readily available, open-source library of data structures written in Java, that comes complete with new implementations of several pre-published algorithms and a basic interactive graphical interface for visualizing planar split networks. SPECTRE also supports the use of longer running algorithms by providing command line interfaces, which can be executed on servers or in High Performance Computing environments. Availability and implementation Full source code is available under the GPLv3 license at: https://github.com/maplesond/SPECTRE . SPECTRE’s core library is available from Maven Central at: https://mvnrepository.com/artifact/uk.ac.uea.cmp.spectre/core . Documentation is available at: http://spectre-suite-of-phylogenetic-tools-for-reticulate-evolution.readthedocs.io/en/latest/ Contact sarah.bastkowski@earlham.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation There are many multiple testing correction methods. Some of them are robust to various dependencies in the data while others are not. Some of the implementations have problems for managing high dimensional list of P -values as currently demanded by microarray and other omic technologies. Results The program Myriads, formerly SGoF+, provides some of the most important P -value-based correction methods jointly with a test of dependency and a P -value simulator. Myriads easily manage hundreds of thousands of P -values. Availability and implementation http://myriads.webs.uvigo.es Contact myriads@uvigo.es Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary We build a software package scHiCNorm that uses zero-inflated and hurdle models to remove biases from single-cell Hi-C data. Our evaluations prove that our models can effectively eliminate systematic biases for single-cell Hi-C data, which better reveal cell-to-cell variances in terms of chromosomal structures. Availability and implementation scHiCNorm is available at http://dna.cs.miami.edu/scHiCNorm/ . Perl scripts are provided that can generate bias features. Pre-built bias features for human (hg19 and hg38) and mouse (mm9 and mm10) are available to download. R scripts can be downloaded to remove biases. Contact zheng.wang@miami.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Microsatellites or Simple Sequence Repeats (SSRs) are short tandem repeats of DNA motifs present in all genomes. They have long been used for a variety of purposes in the areas of population genetics, genotyping, marker-assisted selection and forensics. Numerous studies have highlighted their functional roles in genome organization and gene regulation. Though several tools are currently available to identify SSRs from genomic sequences, they have significant limitations. Results We present a novel algorithm called PERF for extremely fast and comprehensive identification of microsatellites from DNA sequences of any size. PERF is several fold faster than existing algorithms and uses up to 5-fold lesser memory. It provides a clean and flexible command-line interface to change the default settings, and produces output in an easily-parseable tab-separated format. In addition, PERF generates an interactive and stand-alone HTML report with charts and tables for easy downstream analysis. Availability and implementation PERF is implemented in the Python programming language. It is freely available on PyPI under the package name perf_ssr, and can be installed directly using pip or easy_install. The documentation of PERF is available at https://github.com/rkmlab/perf . The source code of PERF is deposited in GitHub at https://github.com/rkmlab/perf under an MIT license. Contact tej@ccmb.res.in Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary We describe ThetaMater , an open source R package comprising a suite of functions for efficient and scalable Bayesian estimation of the population size parameter θ from genomic data. Availability and implementation ThetaMater is available at GitHub ( https://github.com/radamsRHA/ThetaMater ). Contact todd.castoe@uta.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary The NanoString System is a well-established technology for measuring RNA and DNA abundance. Although it can estimate copy number variation, relatively few tools support analysis of these data. To address this gap, we created NanoStringNormCNV, an R package for pre-processing and copy number variant calling from NanoString data. This package implements algorithms for pre-processing, quality-control, normalization and copy number variation detection. A series of reporting and data visualization methods support exploratory analyses. To demonstrate its utility, we apply it to a new dataset of 96 genes profiled on 41 prostate tumour and 24 matched normal samples. Availability and implementation NanoStringNormCNV is implemented in R and is freely available at http://labs.oicr.on.ca/boutros-lab/software/nanostringnormcnv . Contact paul.boutros@oicr.on.ca Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Following the type II CRISPR-Cas9 system, type V CRISPR-Cpf1 endonucleases have been found to be applicable for genome editing in various organisms in vivo . However, there are as yet no web-based tools capable of optimally selecting guide RNAs (gRNAs) among all possible genome-wide target sites. Here, we present Cpf1-Database, a genome-wide gRNA library design tool for LbCpf1 and AsCpf1, which have DNA recognition sequences of 5′-TTTN-3′ at the 5′ ends of target sites. Cpf1-Database provides a sophisticated but simple way to design gRNAs for AsCpf1 nucleases on the genome scale. One can easily access the data using a straightforward web interface, and using the powerful collections feature one can easily design gRNAs for thousands of genes in short time. Availability and implementation Free access at http://www.rgenome.net/cpf1-database/ . Contact sangsubae@hanyang.ac.kr

Description: Motivation Structural studies of TM domains of single-spanning (bitopic) membrane proteins are impeded by their instability, flexibility and heterogeneity. The new computational method TMDOCK allows reliable modeling of homodimers of transmembrane (TM) α-helices on a proteomic scale. Results 3D models of 2129 parallel homodimers formed by TM α-helices of bitopic proteins from six evolutionarily distant organisms were modeled by TMDOCK, verified through experimental data available for nearly 600 proteins, and included in the Membranome database (v.2.0) along with related information to facilitate structural and evolutionary analysis of bitopic proteins. Availability and implementation http://membranome.org Contact almz@umich.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Next Generation Sequencing (NGS) technology enables identification of microbial genomes from massive amount of human microbiomes more rapidly and cheaper than ever before. However, the traditional sequential genome analysis algorithms, tools, and platforms are inefficient for performing large-scale metagenomic studies on ever-growing sample data volumes. Currently, there is an urgent need for scalable analysis pipelines that enable harnessing all the power of parallel computation in computing clusters and in cloud computing environments. We propose ViraPipe, a scalable metagenome analysis pipeline that is able to analyze thousands of human microbiomes in parallel in tolerable time. The pipeline is tuned for analyzing viral metagenomes and the software is applicable for other metagenomic analyses as well. ViraPipe integrates parallel BWA-MEM read aligner, MegaHit De novo assembler, and BLAST and HMMER3 sequence search tools. We show the scalability of ViraPipe by running experiments on mining virus related genomes from NGS datasets in a distributed Spark computing cluster. Results ViraPipe analyses 768 human samples in 210 minutes on a Spark computing cluster comprising 23 nodes and 1288 cores in total. The speedup of ViraPipe executed on 23 nodes was 11x compared to the sequential analysis pipeline executed on a single node. The whole process includes parallel decompression, read interleaving, BWA-MEM read alignment, filtering and normalizing of non-human reads, De novo contigs assembling, and searching of sequences with BLAST and HMMER3 tools. Contact ilari.maarala@aalto.fi Availability and implementation https://github.com/NGSeq/ViraPipe

Description: Motivation As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. Results We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Availability and implementation Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid . Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. Contact JWitte@ucsf.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Bulked segregant analysis combined with next generation sequencing has proven to be a simple and efficient approach for fast mapping of quantitative trait loci (QTLs). However, how to estimate the proportion of phenotypic variance explained by a QTL (or termed QTL heritability) in such pooled QTL mapping is an unsolved problem. Results In this paper, we propose a method called PQHE to estimate QTL heritability using pooled sequencing data obtained under different experimental designs. Simulation studies indicated that our method is correct and feasible. Four practical examples from rice and yeast are demonstrated, each representing a different situation. Availability and implementation The R scripts of our method are open source under GPLv3 license at http://genetics.fafu.edu.cn/PQHE or https://github.com/biotangweiqi/PQHE . The R scripts require the R package rootSolve. Contact wuwr@fafu.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Identifying genomic regions with higher than expected mutation count is useful for cancer driver detection. Previous parametric approaches require numerous cell-type-matched covariates for accurate background mutation rate (BMR) estimation, which is not practical for many situations. Non-parametric, permutation-based approaches avoid this issue but usually suffer from considerable compute-time cost. Hence, we introduce Mutations Overburdening Annotations Tool (MOAT), a non-parametric scheme that makes no assumptions about mutation process except requiring that the BMR changes smoothly with genomic features. MOAT randomly permutes single-nucleotide variants, or target regions, on a relatively large scale to provide robust burden analysis. Furthermore, we show how we can do permutations in an efficient manner using graphics processing unit acceleration, speeding up the calculation by a factor of ∼250. Availability and implementation MOAT is available at moat.gersteinlab.org . Contact mark@gersteinlab.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Protein function is often facilitated by the existence of multiple stable conformations. Structure prediction algorithms need to be able to model these different conformations accurately and produce an ensemble of structures that represent a target’s conformational diversity rather than just a single state. Here, we investigate whether current loop prediction algorithms are capable of this. We use the algorithms to predict the structures of loops with multiple experimentally determined conformations, and the structures of loops with only one conformation, and assess their ability to generate and select decoys that are close to any, or all, of the observed structures. Results We find that while loops with only one known conformation are predicted well, conformationally diverse loops are modelled poorly, and in most cases the predictions returned by the methods do not resemble any of the known conformers. Our results contradict the often-held assumption that multiple native conformations will be present in the decoy set, making the production of accurate conformational ensembles impossible, and hence indicating that current methodologies are not well suited to prediction of conformationally diverse, often functionally important protein regions. Contact marks@stats.ox.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Information about metabolic pathways in a comparative context is one of the most powerful tool to help the understanding of genome-based differences in phenotypes among organisms. Although several platforms exist that provide a wealth of information on metabolic pathways of diverse organisms, the comparison among organisms using metabolic pathways is still a difficult task. Results We present TabPath (Tables for Metabolic Pathway), a web-based tool to facilitate comparison of metabolic pathways in genomes based on KEGG. From a selection of pathways and genomes of interest on the menu, TabPath generates user-friendly tables that facilitate analysis of variations in metabolism among the selected organisms. Availability and implementation TabPath is available at http://200.239.132.160:8686 . Contact lmmorei@gmail.com

Description: A method is described to design a microstructure comprised of multi-Fresnel zone plate (FZP) fragments for shaping an optical needle with arbitrary length. Thus, a microstructure comprised of three planar FZP fragments with different focal lengths f 1 , f 2 , and f 3 is designed to form a long optical needle by delicate interference of coherent light beams diffracted from these three FZP fragments. For a 74.34-μm-diameter microstructure illuminated with a linearly x-polarized beam, a 7.87- λ -long optical needle is produced at a distance of 12.31  λ away from the mask surface. The sizes of transverse beam are 0.97 and 0.4  λ in x and y directions, respectively. For this work, the vectorial angular spectrum (VAS) theory is employed to describe the electric field of light behind the microstructure, as well as the three-dimensional finite-difference time-domain (3D FDTD) method is adopted to further verify the results obtained.

Description: Micro electrical discharge machining (micro EDM) is a proven technology for the manufacturing of miniaturized parts and components. This study uses the Taguchi method with an L 9 orthogonal array, a signal-to-noise (S/N) ratio, and an analysis of variance (ANOVA) to determine the performance characteristics for micro EDM milling operations. The micro EDM milling parameters (pulse duration, pulse off, discharge current, and working period) are optimized using multiple performance characteristics (electrode wear rate (EWR), material removal rate (MRR), and overcut). Grey relational Taguchi analysis is used to optimize the process parameters for micro EDM ofTi–6Al–4V alloy, using tungsten carbide (WC) tool electrodes. The results demonstrate that using a grey-based Taguchi analysis, the EWR decreases from 1.694 × 10 −5 to 1.671 × 10 −5  mm 3 /min, the MRR increases from 6.44 × 10 −4 to 7.23 × 10 −4  mm 3 /min, and the overcut decreases from 0.092 × 10 −1 to 0.088 × 10 −1  mm. There is also a comparative study of the performance of WC, WC-coated Ag (high electrical and thermal properties), WC-coated TiN, and ZrN electrodes (excellent electrical insulation, high melting points, and superior hardness) used for micro EDM milling. A confirmation run shows that the WC electrode-coated Ag thin film yields the highest EWR, MRR, and overcut values. The WC electrodes that are coated with TiN thin film give the lowest EWR and overcut value, which would improve the micro EDM stability and enhance microscale component machining quality.

Description: A novel process was used to fabricate a nanostructured polyurethane-based fabric. Batches were made by extruding the polymerised formulation while it was wound onto a mandrill to produce a spool of nanostructured 1.5–2.5-μm-diameter fibres. During this research, the ultrafine 2-μm-diameter polyurethane yarn has been nanostructured with 0.5, 0.75 and 1% (wt) graphitised multiwalled carbon nanotubes (MWCNTs). The yarn was subsequently weaved to form an electrically conductive fabric, which has ten times the electrical conductivity over its parent yarn. The process in which nanostructured fabric is manufactured is discussed together with its enhanced electrical conductivity characteristics.

Description: Metal cutting (or machining) is one important aspect of the manufacturing system. Selecting optimal cutting conditions for machining is then a crucial process planning task for manufacturing. Traditionally, solving such machining problems was only focused on economic objectives such as maximizing profit or minimizing production time requirement. In the recent decade, however, minimizing energy consumption in manufacturing processes has attracted increased attention due to increasing energy costs and concern with greenhouse gas emissions. Energy loss could be avoided by carefully selecting cutting parameters. This paper develops a multi-objective mathematical model to minimize unit production costs along with energy consumption for face milling operations. In addition, an evolutionary strategy (ES)-based optimization approach is used to identify optimal cutting conditions for the proposed model.

Description: A theoretical formula for surface roughness of layer-based manufactured parts in additive manufacturing is developed considering a more accurate definition of the centerline by minimizing the total arithmetic deviations of the actual surface profile. The developed model is experimentally validated, and it is compared with those that are used in common practices. Considering the uncontrolled process variables and the complexity of the numerical solutions, the analytical and experimental results show satisfying agreement. A methodology is also developed to decide whether the objective surface slope is feasible with the current number of layers and how the layers need to be laid down to achieve the desired surface accuracy. The methodology yields more accurate small features on the surfaces of the layer-based manufactured products.

Description: In this study, the formability of the FML (fiber metal laminate) sheets has been investigated by experimental and numerical methods. The sheets consist of an aluminum skin and a glass fiber reinforced core. Uniaxial tensile and stretch forming tests were performed to extract the forming limit diagram (FLD), experimentally. M-K (Marciniak-Kuczynski) method was implemented to extracting the FLD of the FMLs, numerically. The effect of skin and core thicknesses on formability was studied in variable and constant total thickness by numerical method. Finally, it has been cleared that the numerical model predicts the necking strains with less than 9% error. Also, it has been concluded that with doubling of core thickness in constant and variable total thickness, the average formability improves 15 and 23%, respectively, and with tripling core thickness, these values reach to 26 and 53%. In addition, with twofold increase of skin thickness in constant core thickness, the average formability enhances up to 76%.

Description: Layered extrusion forming, a type of additive manufacturing technique, was applied in this study, using aqueous ceramic pastes to build near-net-shaped ceramic cores at room temperature. As an example, aqueous-based pastes of alumina using methylcellulose (MC) as binder were prepared, and layered extrusion forming method was employed to fabricate green alumina cores. Afterwards, post sintering was carried out to obtain ceramic cores with high strength. Pastes with alumina solid loading varying from 30 to 52 vol.% were dispersed by MC solutions and presented shear-thinning rheological behaviors. Solid loading had a significant effect on the processing of layered extrusion formation. Alumina pastes with 30 to 46 vol.% were not suitable for layered extrusion formation, while pastes with solid loading ranging from 48 to 52 vol.% could be applied successfully to the layered extrusion forming process. Pastes composed of 2 wt.% MC solution as binder and 50 vol.% solid loading generated specimens with best shape retention and surface consistency, and the height of each layer extruded was equally distributed. The green specimens exhibited slightly smaller dimensions than the designed ones, and sintered specimens showed linear shrinkage. Sintered specimens presented homogeneous morphology, and no gap between extruded filaments was observed. Specimens sintered at 1600 °C for 180 mins possessed best comprehensive performances and low thermal expansion, which could meet the requirements of ceramic cores in alloys casting. The systematic study of layered extrusion forming indicates that this method is a promising method to fabricate ceramic cores.

Description: Nowadays, increased concerns about energy-efficient and environmentally benign manufacturing process have become an emerging issue. Deep drawing is a widely used traditional process especially in the automotive and aeronautical industries, which is also energy intensive and accounts for a large portion of energy consumption in metal forming sectors. The investigation of sheet metal forming energy saving is worth studying and, within this research field, a certain lack of energy prediction models or tools is still present. The aim of this research was to contribute towards the development of analytical models and logic for estimating the energy consumption in sheet metal forming processes, which could provide the basic for a significant energy reduction. An application to a spherical part deep drawing process was used to verify the validity and rationality of the models with the results deviation within 10%. And the effects of the process parameters on energy consumption were discussed. The overall contribution of the main process parameters on energy consumption was analyzed through a matrix of experiments. The obtained results allowed to be used for guiding process optimization of energy saving and the selection of manufacturing parameters.

Description: Summary ArachnoServer is a manually curated database that consolidates information on the sequence, structure, function and pharmacology of spider-venom toxins. Although spider venoms are complex chemical arsenals, the primary constituents are small disulfide-bridged peptides that target neuronal ion channels and receptors. Due to their high potency and selectivity, these peptides have been developed as pharmacological tools, bioinsecticides and drug leads. A new version of ArachnoServer (v3.0) has been developed that includes a bioinformatics pipeline for automated detection and analysis of peptide toxin transcripts in assembled venom-gland transcriptomes. ArachnoServer v3.0 was updated with the latest sequence, structure and functional data, the search-by-mass feature has been enhanced, and toxin cards provide additional information about each mature toxin. Availability and implementation http://arachnoserver.org Contact support@arachnoserver.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Grinding chatter is a kind of self-excited vibration which does not need external motivation. However, the internal motivation exists in the form of the grinding force. The grinding force is time-varying due to the regeneration effects between the grinding wheel and the workpiece. Generally, the chatter strength is reflected by the grinding force, which affects the distribution of the grinding temperature field by adjusting the heat flux. Moreover, the dynamic temperature generated within the grinding area is applied to cause the materials microstructure transformation of the workpiece’s surface. Consequently, the materials microstructure transformation of the workpiece’s surface is affected by the grinding chatter as well. In order to investigate the relationship between the material microstructure transformation and the chatter strength, a new hybrid model which combines the finite difference method (FDM) and the cellular automaton (CA) method is established accordingly. Coupled by the chatter factor, the FDM verified by the Jager theory model is applied to obtain the distribution of the dynamic grinding temperature field. It can be proved that the grinding temperature goes up with the increase of the grinding chatter. Combined with the dynamic temperature field, the CA model is firstly applied to simulate the austenization which includes the nucleation, growth, grain coarsening, and carbon diffusion process. The results indicate that the grain size expands with the increase of the grinding chatter, which results in the low mechanical properties of the material. Then, the martensite’s nucleation with the grinding chatter started in the calculated austenite microstructure. Afterwards, the martensite with the grinding chatter grows up in the calculated austensite microstructure. From the experiment and the simulation, it can be concluded that the martensite content decreases with the increase of the grinding chatter. According to the above conclusions, it proves that better mechanical properties can be obtained by wakening the impact of the grinding chatter.

Description: To improve the shaping efficiency and the accuracy of ceramic ball blanks, an oscillating-plate shaping method was developed. In the new shaping process, the ceramic ball blanks were pre-sintered to obtain enough strength to resist the crushing force, and then processed by the oscillating-plate shaping method. Owing to the improvements of sphericity and the size consistency of blanks, the efficiency of the followed lapping process for the fully sintered ceramic balls will be enhanced significantly. The motion of ball blank in the shaping process was investigated through kinematic simulation based on ADAMS model. Shaping experiments of Ø 13 mm pre-sintered Si 3 N 4 ceramic ball blanks were carried out. The average diameter of ball blank decreased from 13.097 ± 0.092 mm (Avg. ± SD.) to 12.407 ± 0.007 mm, and the sphericity was improved from113.9 ± 42.7 to 4.5 ± 1.6 μm in 45-min shaping. The lapping experiments to reduce the sphericity error of shaped and unshaped ceramic balls after the full sintering to 1 μm were also carried out. It takes 55 h for the shaped blanks and 135 h for the unshaped blanks respectively. The results show that the oscillating-plate shaping is a promising method to improve the processing efficiency of ceramic balls significantly by the improvements of the sphericity and size consistency of the as-received ceramic ball blank.

Description: Flood cooling is a typical cooling strategy used in industry to dissipate the high temperature generated during machining of Inconel 718. The use of flood coolant has risen environmental and health concerns which call for different alternatives. Minimum quaintly lubricant (MQL) has been successfully introduced as an acceptable coolant strategy; however, its potential to dissipate heat is much lower than the one achieved using flood coolant. MQL-nano-cutting fluid is one of the suggested techniques to further improve the performance of MQL particularly when machining difficult-to-cut materials. The main objective of this study is to investigate the effects of two types of nano-cutting fluids on tool performance and chip morphology during turning of Inconel 718. Multi-walled carbon nanotubes (MWCNTs) and aluminum oxide (Al 2 O 3 ) gamma nanoparticles have been utilized as nano-additives. The novelty here lies on enhancing the MQL heat capacity using different nano-additives-based fluids in order to improve Inconel 718 machinability. In this investigation, both nano-fluids showed better results compared to the tests performed without any nano-additives. Significant changes in modes of tool wear and improvement in the intensity of wear progression have been observed when using nano-fluids. Also, the collected chips have been analyzed to understand the effects of adding nano-additives on the chip morphology. Finally, it has been found that MWCNT nano-fluid has shown better performance than Al 2 O 3 nano-fluid.

Description: In robotic grinding, significant tool deflection occurs due to the lower stiffness of the manipulator and tool, compared with operation by universal grinding machines. Tool deflection during robotic grinding operation causes geometrical errors in the workpiece cross section. Also, it makes difficult to control the grinding cutting depth. In this study, a method is proposed for calculation of the tool deflection in normal and tangential directions based on grinding force feedback in these directions. Based on calculated values, a real-time tool deflection compensation (TDC) algorithm is developed and implemented. Force interaction between the tool and workpiece is significant for grinding operation. Implementing grinding with constant normal force is a well-known approach for improving surface quality. Tool deflection in the robotic grinding causes orientation between the force sensor reference frame and tool reference frame. This means that the measured normal and tangential forces by the sensor are not actual normal and tangential interaction forces between the tool and workpiece. In order to eliminate this problem, a resultant grinding force control strategy is designed and implemented for a parallel hexapod-robotic light abrasive surface grinding operation. Due to the nonlinear nature of the grinding operation, a supervised fuzzy controller is designed where the reference input is identified by the developed grinding force model. This grinding model is optimized for the robotic grinding operation considering setup stiffness. Evaluation of the experimental results demonstrates significant improvement in grinding operation accuracy using the proposed resultant force control strategy in parallel with a real-time TDC algorithm.

Description: The suitable thermal, chemical, and corrosion resistance properties of glass make it possible to be used in a wide variety of product manufacturing, like lenses, mirrors, mold, semiconductor, biomedical, optical, and micro-electronics. However, machining of glass like any brittle material has big challenges owing to its inherent brittleness. Ductile mode machining is known to promote the material removal from a brittle material in ductile manner rather than by brittle fracture. In high-speed machining, the thermal softening effects can enhance flexibility in ductile machining of brittle materials. In this paper, an analytical model is developed to predict the amount of temperature generated in the immediate next removable layer (INRL) of the soda-lime glass work piece per unit depth of cut \( \Delta {\overline{T}}_{\mathrm{INRL}} \) based on fundamental micro-machining principle and material physical properties. The model incorporates the effects of cutting speed, feed rate, strain rate, and thermal softening effect. The simulation and experimental results showed that at high cutting speed, glass softening can be achieved by adiabatic heating in order to facilitate ductile machining. The amount of adiabatic heating can be controlled by predicting the amount of the \( \Delta {\overline{T}}_{\mathrm{INRL}} \) .

Description: Most computer numerically controlled (CNC) machine tools with turning capability use turning tools with clamped inserts due to their high precision and ease of replacement. However, although the handles of the turning tools are marked with their specification details, such labels do not appear on the inserts themselves and, thus, often lead to misplacement and installation of incorrect inserts. Accordingly, many researchers have proposed imaging systems based on scanners, single cameras, or microscopes for the automatic measurement and identification of inserts. However, such systems require that the inserts be unloaded from the turning tool and positioned precisely in front of the imaging system. Consequently, online measurement is impossible. This study thus proposes a three-dimensional (3D) vision system capable of identifying inserts in situ based on 3D measurements. Specifications such as insert angles, edge lengths, and nose radii of each insert were identified. The feasibility of the proposed system is demonstrated by identifying the specifications of nine types of insert. The experimental results show that the system achieves an average recognition rate of 98.89% for insert angles, 95.56% for cutting edge lengths, and 92.22% for nose radii.

Description: This article presents the results of deploying a new approach for additive manufacturing (AM) functionally graded titanium (Ti) cellular structures with selectively built closed cells, which tailor the mechanical behavior of structures to mimic the similar properties of bones. A hybrid AM system, which combines two methods of binder jetting and material extrusion, was applied. Ti parts were fabricated from powder-bed AM (binder jetting) with encapsulated sacrificial polymeric droplets within the layers and at designated locations to form a periodic pattern. After a heat treatment step, which resulted in the polymer decomposition, the measurement of porosity among the specimens demonstrated the range of 6 to 16% enhancement in these parameters in samples with periodic cells compared to others. Additionally, micro-computerized tomography was performed on the candidate samples to determine the pore morphology and layout within the specimens. The measurement of stiffness and yield stress suggested a range of 2.48 ± 0.37 to 3.55 ± 0.49 GPa and 107.65 ± 18.14 to 145.75 ± 13.85 MPa, respectively. These results suggested that the statistical significance occurred in the batches with periodic cells.

Description: The extrusion system is an integral part of any fused deposition style 3D printing technique. However, the extruder designs found in commercial and hobbyist printers are mostly suitable for materials in filament form. While printing with a filament is not a problem per se, the printing of materials that may not be readily available in the filament form or not commercially viable remains untapped, e.g., biopolymers and material blends. This is particularly an issue in the research and hobbyist space where the capability of printing a variety of materials or materials recycled from already printed parts may be of utmost importance. This paper presents a pellet-based extrusion system for the 3D printing of biopolymers. The system has been designed from the first principles and therefore can be extended to other materials with parameter adjustments or slight hardware modifications. A robust mechatronic design has been realized using an unconventional yet simplistic approach. The extrusion system uses a series of control factors to generate a consistent output of material over the course of a print. The platform and surrounding processes are set up so that software can be used to define the printing parameters; this allows a simpler adaption to different materials. The utility of the extruder is demonstrated through extensive printing and testing of the printed parts.