The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSIBLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

/pdf/gapped-blast-and-psi-blast-a-new-generation-of-protein-2ybl67x5yx.pdf

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.

/pdf/the-protein-data-bank-4lbxxcio6v.pdf

The Protein Data Bank

Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

/pdf/star-ultrafast-universal-rna-seq-aligner-r1vlt61g3c.pdf

STAR: ultrafast universal RNA-seq aligner

Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

/pdf/ultrafast-and-memory-efficient-alignment-of-short-dna-3mrm42jmnc.pdf

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

/pdf/blurring-the-boundaries-between-the-scientific-papers-and-21j7o3x0su.pdf

Blurring the boundaries between the scientific 'papers' and biological databases

Motivation: Proteomics researchers need to be able to quickly retrieve relevant information from the web and the biomedical literature. To improve information retrieval, we leverage the structure of the semantic web, developing an approach for joining it with the largely opposing paradigm of unsupervised web search.

Results: Our approach uses a Resource-Description-Framework (RDF) graph that inter-relates documents through their associated biological identifiers (e.g., protein ID). A search begins with a simple query term (UniProt identifier), which is expanded with terms extracted from documents in the RDF graph surrounding the query (“the subgraph”). We re-rank documents in the full corpus (e.g. all PubMed) by their cosine-similarity scores against a composite word-weight vector created from the subgraph. This vector is a weighted sum of individual word-weight vectors for documents at each node of the subgraph, taking into account the types of relationships between the central query identifier and the nodes connected to it. The computation also uses inverse document frequency (IDF) in a novel way to rescale the local word frequencies in the query's subgraph relative to that in other subgraphs. Applying our procedure to PubMed, we optimize weights for various relationships in the subgraph and benchmark overall performance in detail. Using a subgraph containing family relationships (from PFAM) results in a significant improvement in accuracy (as compared to not considering the subgraph in the search) when assessed against known relationships in the yeast literature. Moreover, we achieve this accuracy using only relatively simple and computationally efficient methods.

Contact: mark.gerstein@yale.edu

Supplementary information: http://hub.gersteinlab.org/ir-supp/

http://archive.gersteinlab.org/papers/e-print/semweb-ir/preprint.pdf

Leveraging the structure of the Semantic Web to enhance information retrieval for proteomics

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation and disease. Their binding sites cover more of the genome than coding exons; nevertheless, most noncoding variant prioritization methods only focus on transcriptional regulation. Here, we integrate the portfolio of ENCODE-RBP experiments to develop RADAR, a variant-scoring framework. RADAR uses conservation, RNA structure, network centrality, and motifs to provide an overall impact score. Then, it further incorporates tissue-specific inputs to highlight disease-specific variants. Our results demonstrate RADAR can successfully pinpoint variants, both somatic and germline, associated with RBP-function dysregulation, which cannot be found by most current prioritization methods, for example, variants affecting splicing.

/pdf/radar-annotation-and-prioritization-of-variants-in-the-post-1b62ynwg84.pdf

RADAR: annotation and prioritization of variants in the post-transcriptional regulome of RNA-binding proteins.

Alternative RNA splicing provides an important means to expand metazoan transcriptome diversity. Contrary to what was accepted previously, splicing is now thought to predominantly take place during transcription. Motivated by emerging data showing the physical proximity of the spliceosome to Pol II, we surveyed the effect of epigenetic context on co-transcriptional splicing. In particular, we observed that splicing factors were not necessarily enriched at exon junctions and that most epigenetic signatures had a distinctly asymmetric profile around known splice sites. Given this, we tried to build an interpretable model that mimics the physical layout of splicing regulation where the chromatin context progressively changes as the Pol II moves along the guide DNA. We used a recurrent-neural-network architecture to predict the inclusion of a spliced exon based on adjacent epigenetic signals, and we showed that distinct spatio-temporal features of these signals were key determinants of model outcome, in addition to the actual nucleotide sequence of the guide DNA strand. After the model had been trained and tested (with >80% precision-recall curve metric), we explored the derived weights of the latent factors, finding they highlight the importance of the asymmetric time-direction of chromatin context during transcription.

https://biorxiv.org/content/biorxiv/early/2020/02/03/2020.02.03.932251.full-text.pdf

Epigenome-based Splicing Prediction using a Recurrent Neural Network

The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis. This Perspective highlights privacy issues related to the sharing of functional genomics data, including genotype and phenotype information leakage from different functional genomics data types and their summarization steps. The authors also review the techniques that will enable broad sharing and analysis while maintaining privacy.

Mark Gerstein

Papers

Blurring the boundaries between the scientific 'papers' and biological databases

Leveraging the structure of the Semantic Web to enhance information retrieval for proteomics

RADAR: annotation and prioritization of variants in the post-transcriptional regulome of RNA-binding proteins.

Epigenome-based Splicing Prediction using a Recurrent Neural Network

Functional genomics data: privacy risk assessment and technological mitigation.