MOTIVATION: De novo mutations (i.e. newly occurring mutations) are a pre-dominant cause of sporadic dominant monogenic diseases and play a significant role in the genetics of complex disorders. De novo mutation studies also inform population genetics models and shed light on the biology of DNA replication and repair. Despite the broad interest, there is room for improvement with regard to the accuracy of de novo mutation calling. RESULTS: We designed novoCaller, a Bayesian variant calling algorithm that uses information from read-level data both in the pedigree and in unrelated samples. The method was extensively tested using large trio-sequencing studies, and it consistently achieved over 97% sensitivity. We applied the algorithm to 48 trio cases of suspected rare Mendelian disorders as part of the Brigham Genomic Medicine gene discovery initiative. Its application resulted in a significant reduction in the resources required for manual inspection and experimental validation of the calls. Three de novo variants were found in known genes associated with rare disorders, leading to rapid genetic diagnosis of the probands. Another 14 variants were found in genes that are likely to explain the phenotype, and could lead to novel disease-gene discovery. AVAILABILITY AND IMPLEMENTATION: Source code implemented in C++ and Python can be downloaded from https://github.com/bgm-cwg/novoCaller. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Proteomics presents the opportunity to provide novel insights about the global biochemical state of a tissue. However, a significant problem with current methods is that shotgun proteomics has limited success at detecting many low abundance proteins, such as transcription factors from complex mixtures of cells and tissues. The ability to assay for these proteins in the context of the entire proteome would be useful in many areas of experimental biology. RESULTS: We used network-based inference in an approach named SNIPE (Software for Network Inference of Proteomics Experiments) that selectively highlights proteins that are more likely to be active but are otherwise undetectable in a shotgun proteomic sample. SNIPE integrates spectral counts from paired case-control samples over a network neighbourhood and assesses the statistical likelihood of enrichment by a permutation test. As an initial application, SNIPE was able to select several proteins required for early murine tooth development. Multiple lines of additional experimental evidence confirm that SNIPE can uncover previously unreported transcription factors in this system. We conclude that SNIPE can enhance the utility of shotgun proteomics data to facilitate the study of poorly detected proteins in complex mixtures. AVAILABILITY AND IMPLEMENTATION: An implementation for the R statistical computing environment named snipeR has been made freely available at http://genetics.bwh.harvard.edu/snipe/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Although many methods and statistical approaches have been developed for protein identification by mass spectrometry, the problem of accurate assessment of statistical significance of protein identifications remains an open question. The main issues are as follows: (i) statistical significance of inferring peptide from experimental mass spectra must be platform independent and spectrum specific and (ii) individual spectrum matches at the peptide level must be combined into a single statistical measure at the protein level. RESULTS: We present a method and software to assign statistical significance to protein identifications from search engines for mass spectrometric data. The approach is based on asymptotic theory of order statistics. The parameters of the asymptotic distributions of identification scores are estimated for each spectrum individually. The method relies on new unbiased estimators for parameters of extreme value distribution. The estimated parameters are used to assign a spectrum-specific P-value to each peptide-spectrum match. The protein-level confidence measure combines P-values of peptide-to-spectrum matches. CONCLUSION: We extensively tested the method using triplicate mouse and yeast high-throughput proteomic experiments. The proposed statistical approach improves the sensitivity of protein identifications without compromising specificity. While the method was primarily designed to work with Mascot, it is platform-independent and is applicable to any search engine which outputs a single score for a peptide-spectrum match. We demonstrate this by testing the method in conjunction with X!Tandem. AVAILABILITY: The software is available for download at ftp://genetics.bwh.harvard.edu/SSPV/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

One of the major goals of comparative genomics is to understand the evolutionary history of each nucleotide in the human genome sequence, and the degree to which it is under selective pressure. Ascertainment of selective constraint at nucleotide resolution is particularly important for predicting the functional significance of human genetic variation and for analyzing the sequence substructure of cis-regulatory sequences and other functional elements. Current methods for analysis of sequence conservation are focused on delineation of conserved regions comprising tens or even hundreds of consecutive nucleotides. We therefore developed a novel computational approach designed specifically for scoring evolutionary conservation at individual base-pair resolution. Our approach estimates the rate at which each nucleotide position is evolving, computes the probability of neutrality given this rate estimate, and summarizes the result in a Sequence CONservation Evaluation (SCONE) score. We computed SCONE scores in a continuous fashion across 1% of the human genome for which high-quality sequence information from up to 23 genomes are available. We show that SCONE scores are clearly correlated with the allele frequency of human polymorphisms in both coding and noncoding regions. We find that the majority of noncoding conserved nucleotides lie outside of longer conserved elements predicted by other conservation analyses, and are experiencing ongoing selection in modern humans as evident from the allele frequency spectrum of human polymorphism. We also applied SCONE to analyze the distribution of conserved nucleotides within functional regions. These regions are markedly enriched in individually conserved positions and short (<15 bp) conserved "chunks." Our results collectively suggest that the majority of functionally important noncoding conserved positions are highly fragmented and reside outside of canonically defined long conserved noncoding sequences. A small subset of these fragmented positions may be identified with high confidence.

Article Analysis of the position dependent amino acid probabilities and its application to the search for remote homologues Share on Authors: Shamil R. Sunyaev VA Engelhardt Institut of Molecular Biology, Russian Academy of Sciences, Vavilov Street 32, 117984 Moscow, Russia and European Molecular Biology Laboratory, Meyerhofstrassel, Postfach 10.2209, D-69012 Heidelberg, Fed.Rep. Germany and Max-Delbruck-Centrum fur Molekulare Medizin, Robert-Rossle-Strasse 34, D-13122 Berlin-Buch, Fed. Rep. Germany VA Engelhardt Institut of Molecular Biology, Russian Academy of Sciences, Vavilov Street 32, 117984 Moscow, Russia and European Molecular Biology Laboratory, Meyerhofstrassel, Postfach 10.2209, D-69012 Heidelberg, Fed.Rep. Germany and Max-Delbruck-Centrum fur Molekulare Medizin, Robert-Rossle-Strasse 34, D-13122 Berlin-Buch, Fed. Rep. GermanyView Profile , Igor V. Rodchnkov VA Engelhardt Institut of Molecular Biology, Russian Academy of Sciences, Vavilov Street 32, 117984 Moscow, Russia and Moscow Institute of Physics and Technology, Institutsky per. 9, Dolgoprudny, Moscow region, Russia VA Engelhardt Institut of Molecular Biology, Russian Academy of Sciences, Vavilov Street 32, 117984 Moscow, Russia and Moscow Institute of Physics and Technology, Institutsky per. 9, Dolgoprudny, Moscow region, RussiaView Profile , Frank Eisenhaber European Molecular Biology Laboratory, Meyerhofstrassel, Postfach 10.2209, D-69012 Heidelberg, Fed.Rep. Germany and Max-Delbruck-Centrum fur Molekulare Medizin, Robert-Rossle-Strasse 34, D-13122 Berlin-Buch, Fed.Rep. Germany European Molecular Biology Laboratory, Meyerhofstrassel, Postfach 10.2209, D-69012 Heidelberg, Fed.Rep. Germany and Max-Delbruck-Centrum fur Molekulare Medizin, Robert-Rossle-Strasse 34, D-13122 Berlin-Buch, Fed.Rep. GermanyView Profile , Eugene N. Kuznetsov Institute of Control Sciences, Russian Academy of Sciences, Profsoyuznaya Street 65, 117806 Moscow, Russia Institute of Control Sciences, Russian Academy of Sciences, Profsoyuznaya Street 65, 117806 Moscow, RussiaView Profile Authors Info & Claims RECOMB '98: Proceedings of the second annual international conference on Computational molecular biologyMarch 1998 Pages 258–265https://doi.org/10.1145/279069.279127Online:01 March 1998Publication History 2citation249DownloadsMetricsTotal Citations2Total Downloads249Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access