SUMMARY: The quantification of RNA sequencing (RNA-seq) abundance using a normalization method that calculates transcripts per million (TPM) is a key step to compare multiple samples from different experiments. TPMCalculator is a one-step software to process RNA-seq alignments in BAM format and reports TPM values, raw read counts and feature lengths for genes, transcripts, exons and introns. The program describes the genomic features through a model generated from the gene transfer format file used during alignments reporting of the TPM values and the raw read counts for each feature. In this paper, we show the correlation for 1256 samples from the TCGA-BRCA project between TPM and FPKM reported by TPMCalculator and RSeQC. We also show the correlation for raw read counts reported by TPMCalculator, HTSeq and featureCounts. AVAILABILITY AND IMPLEMENTATION: TPMCalculator is freely available at https://github.com/ncbi/TPMCalculator. It is implemented in C++14 and supported on Mac OS X, Linux and MS Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Transcription factors (TFs) form complexes that bind regulatory modules (RMs) within DNA, to control specific sets of genes. Some transcription factor binding sites (TFBSs) near the transcription start site (TSS) display tight positional preferences relative to the TSS. Furthermore, near the TSS, RMs can co-localize TFBSs with each other and the TSS. The proportion of TFBS positional preferences due to TFBS co-localization within RMs is unknown, however. ChIP experiments confirm co-localization of some TFBSs genome-wide, including near the TSS, but they typically examine only a few TFs at a time, using non-physiological conditions that can vary from lab to lab. In contrast, sequence analysis can examine many TFs uniformly and methodically, broadly surveying the co-localization of TFBSs with tight positional preferences relative to the TSS. RESULTS: Our statistics found 43 significant sets of human motifs in the JASPAR TF Database with positional preferences relative to the TSS, with 38 preferences tight (±5 bp). Each set of motifs corresponded to a gene group of 135 to 3304 genes, with 42/43 (98%) gene groups independently validated by DAVID, a gene ontology database, with FDR < 0.05. Motifs corresponding to two TFBSs in a RM should co-occur more than by chance alone, enriching the intersection of the gene groups corresponding to the two TFs. Thus, a gene-group intersection systematically enriched beyond chance alone provides evidence that the two TFs participate in an RM. Of the 903 = 43*42/2 intersections of the 43 significant gene groups, we found 768/903 (85%) pairs of gene groups with significantly enriched intersections, with 564/768 (73%) intersections independently validated by DAVID with FDR < 0.05. A user-friendly web site at http://go.usa.gov/3kjsH permits biologists to explore the interaction network of our TFBSs to identify candidate subunit RMs. CONCLUSIONS: Gene duplication and convergent evolution within a genome provide obvious biological mechanisms for replicating an RM near the TSS that binds a particular TF subunit. Of all intersections of our 43 significant gene groups, 85% were significantly enriched, with 73% of the significant enrichments independently validated by gene ontology. The co-localization of TFBSs within RMs therefore likely explains much of the tight TFBS positional preferences near the TSS.

MOTIVATION: DNA and protein patterns are usefully represented by sequence logos. However, the methods for logo generation in common use lack a proper statistical basis, and are non-optimal for recognizing functionally relevant alignment columns. RESULTS: We redefine the information at a logo position as a per-observation multiple alignment log-odds score. Such scores are positive or negative, depending on whether a column's observations are better explained as arising from relatedness or chance. Within this framework, we propose distinct normalized maximum likelihood and Bayesian measures of column information. We illustrate these measures on High Mobility Group B (HMGB) box proteins and a dataset of enzyme alignments. Particularly in the context of protein alignments, our measures improve the discrimination of biologically relevant positions. AVAILABILITY AND IMPLEMENTATION: Our new measures are implemented in an open-source Web-based logo generation program, which is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/logoddslogo/index.html. A stand-alone version of the program is also available from this site. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Jingchu Luo is a Professor in the College of Life Sciences and Center for Bioinformatics, Peking University, China. He is the EMBnet China Node manager and a long-term friend of Jack Leunissen. Harm Nijveen is a scientific programmer at Wageningen University and Research Center, the Netherlands. He had been working with Jack Leunissen for several years. Teresa Attwood is a Professor of Bioinformatics in the Faculty of Life Sciences and School of Computer Science at the University of Manchester, UK. She is the current Chair of the EMBnet Executive Board. David Judge is a bioinformatics trainer in Cambridge University and a long-term friend of Jack Leunissen. Sandor Pongor is a Group Leader of Protein Structure and Bioinformatics, the International Center for Genetic Engineering and Biotechnology (ICGEB), Italy. He organized bioinformatics courses every June in ICGEB. David Landsman is Chief of the Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health. Martin Bishop is the Managing Editor of Briefings in Bioinformatics. It has already been a year since Jack Leunissen, a former member of the editorial board of Briefings in Bioinformatics, passed away on 14 May 2012. Professor Dr Jack A.M. Leunissen was one of the pioneers of bioinformatics in the Netherlands, in Europe and around the world. Together with senior members of the European Molecular Biology Network (EMBnet), Jack worked with Henry Stewart Publications to launch this journal in 2000, aiming to publish review articles in bioinformatics to benefit the ever-growing community of bioinformatics researchers. He served as a member of the editorial board for >10 years, devoting much of his time to reviewing manuscripts and providing constructive feedback. Born on 1 August 1954 and educated as a biochemist in the 1980s, Jack showed an interest in bioinformatics early in his scientific career. He created the Biocomputing Environment at the University of Nijmegen in 1982, and his PhD thesis, ‘Computer Applications in Protein and Nucleic Acid Sequence Analysis’ in 1989 was one of the earliest in the biocomputing community. During his PhD studies, Jack started the biocomputing section of the Dutch National academic facility for computer-assisted organic synthesis and modelling and worked there for >10 years. In that period, Jack was also strongly involved in the establishment of EMBnet. He was manager of his country’s node and served as Chairman of the Executive Board of EMBnet from 1999 to 2003. In 2000, he was appointed as senior lecturer in bioinformatics at the Centre for Molecular and Biomolecular Informatics at Nijmegen University. At the end of 2002, he accepted the position of Chair in Bioinformatics at the Department of Plant Sciences of the Wageningen University and Research Center. At the same time, he was involved in the newly formed Netherlands Bioinformatics Centre as project leader, member of the National Advisory Committee and member of the Management Team. Jack had a broad interest in bioinformatics related topics, which he was able to pass on to his PhD and MSc students, working on diverse subjects such as text mining, protein orthology, comparative anatomy, web services and Bayesian networks. His curiosity put him in touch with experts from various related disciplines like computer science, mathematics, statistics and linguistics. Even during the tenure of his professorship, he enjoyed exploring new programming languages and technologies to evaluate their usefulness for bioinformatics. Despite his illness, he continued to be actively involved in the work of his students until the end, including reviewing the article ‘Understanding and Identification of Amino Acid Repeats’ published in this issue by his students. It is hard to overstate the contribution that Jack made to bioinformatics education and training, especially to end users coming from biological backgrounds. From the mid 80s, he taught on numerous courses, workshops and conferences within and outside Europe. Together with other colleagues, he taught on the Bioinformatics summer school at the International Center for Genetic Engineering and Biotechnology in Trieste almost every year from 1993. He was also invited to teach several courses in Peking University, where the Chinese EMBnet node is located. Those he taught will never forget his enthusiasm and inspirational style in the class room. In May 2012, he received a prestigious award from the Dutch industry platform for his activities towards making education and training software and databases more accessible for everybody. Jack, we miss you greatly.

[No abstract available]

BACKGROUND: Many DNA regulatory elements occur as multiple instances within a target promoter. Gibbs sampling programs for finding DNA regulatory elements de novo can be prohibitively slow in locating all instances of such an element in a sequence set. RESULTS: We describe an improvement to the A-GLAM computer program, which predicts regulatory elements within DNA sequences with Gibbs sampling. The improvement adds an optional "scanning step" after Gibbs sampling. Gibbs sampling produces a position specific scoring matrix (PSSM). The new scanning step resembles an iterative PSI-BLAST search based on the PSSM. First, it assigns an "individual score" to each subsequence of appropriate length within the input sequences using the initial PSSM. Second, it computes an E-value from each individual score, to assess the agreement between the corresponding subsequence and the PSSM. Third, it permits subsequences with E-values falling below a threshold to contribute to the underlying PSSM, which is then updated using the Bayesian calculus. A-GLAM iterates its scanning step to convergence, at which point no new subsequences contribute to the PSSM. After convergence, A-GLAM reports predicted regulatory elements within each sequence in order of increasing E-values, so users have a statistical evaluation of the predicted elements in a convenient presentation. Thus, although the Gibbs sampling step in A-GLAM finds at most one regulatory element per input sequence, the scanning step can now rapidly locate further instances of the element in each sequence. CONCLUSION: Datasets from experiments determining the binding sites of transcription factors were used to evaluate the improvement to A-GLAM. Typically, the datasets included several sequences containing multiple instances of a regulatory motif. The improvements to A-GLAM permitted it to predict the multiple instances.