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.

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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

Using a PCR (polymerase chain reaction) process to design and produce DNA microarray slides necessitates tracking thousands of samples over a long period of time. In addition, up-to-date information about the samples must be readily available when analyzing experiments done with the resulting microarray slides. A human chromosome 22 project (HC22) at Yale's Center for Excellence in Genomic Sciences (CEGS) used manual sample tracking and handcrafted data files for analyzing the array. The difficulties of manual sample tracking on this project limited the array layout and increased the development time. For subsequent microarray development projects at the Yale CEGS it was decided to build the comprehensive Sample Tracking and Analysis Research Support (STARS) System described herein. The system had to be portable since there are many projects at Yale and elsewhere that require automated microarray sample tracking. Informatics issues in designing and implementing CEGS/STARS are described. The system tracks plates, PCR scoring, microarray slides, and slide printings as core features. CEGS/STARS can support multiple projects and currently there are three sample tracking projects based on the same code and data base design: HC22 and HPR (human promoter) for projects in the Yale CEGS Center, and FLY for projects in the Yale School of Medicine Drosophila Center. In addition to the core tracking features, CEGS/STARS also tracks the biological experiments performed with the resulting microarrays. CEGS/STARS enhances the PCR-based array production protocol with a "cherry picking" algorithm that supports dynamic spot site decisions. Other features include: laboratory robot script generation, lab equipment file generation, automated PCR process scoring, automated links to bioinformatics data analysis tools, annotation and re-annotation of DNA fragments, links to genome databases, and publishing results to GEO. The advanced features of the system are currently being used to produce a human genome promoter array, a drosophila tiling array, and a drosophila promoter array. The source code is available to interested researchers.

Design Issues in Implementing a Portable Sample Tracking and Analysis Research Support (STARS) System for PCR Based Microarray Research

European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK e-mail: thornton@ebi.ac.uk Introduction In what follows, we give an overview of the eight reviews in the Sequences and topology section, describing and summarizing their content. Basically, the reviews this year follow the overall progression in bioinformatics from genome sequence to functional genomics analysis to protein structure. We start with reviews by Karlin and Lancet on genome analysis, the former focusing on more global issues and the latter on what can be learned from specific families. Then, the review by Orengo discusses mapping from the genome to protein families and structure. Stolovitzky talks about analyzing expression information, functional genomics data that characterize the sequence products. The next group of reviews, by Eisenberg and Janin, discuss protein–protein interactions, the interactome, and the current excitement in interpreting this. Finally, we have reviews by Jackson and Westhead, and Nakamura on more detailed aspects of three-dimensional site analysis.

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Sequences and topology Editorial overview

Deep learning methods have achieved state-of-the-art performance in many domains of artificial intelligence, but are typically hard to interpret. Network interpretation is important for multiple reasons, including knowledge discovery, hypothesis generation, fairness and establishing trust. Model transformations provide a general approach to interpreting a trained network post-hoc: the network is approximated by a model, which is typically compressed, whose structure can be more easily interpreted in some way (we call such approaches interpretability schemes). However, the relationship between compression and interpretation has not been fully explored: How much should a network be compressed for optimal extraction of interpretable information? Should compression be combined with other criteria when selecting model transformations? We investigate these issues using two different compression-based schemes, which aim to extract orthogonal kinds of information, pertaining to feature and data instance-based groupings respectively. The first (rank projection trees) uses a structured sparsification method such that nested groups of features can be extracted having potential joint interactions. The second (cascaded network decomposition) splits a network into a cascade of simpler networks, allowing groups of training instances with similar characteristics to be extracted at each stage of the cascade. We use predictive tasks in cancer and psychiatric genomics to assess the ability of these approaches to extract informative feature and data-point groupings from trained networks. We show that the generalization error of a network provides an indicator of the quality of the information extracted; further we derive PAC-Bayes generalization bounds for both schemes, which we show can be used as proxy indicators, and can thus provide a criterion for selecting the optimal compression. Finally, we show that the PAC-Bayes framework can be naturally modified to incorporate additional criteria alongside compression, such as prior knowledge based on previous models, which can enhance interpretable model selection.

https://biorxiv.org/content/10.1101/2020.10.27.358226v1.full.pdf

Compression-based Network Interpretability Schemes

Abstract Treatment with immune checkpoint inhibitors has altered the course of malignant melanoma, with approximately half of the patients with advanced disease surviving for more than 5 years after diagnosis. Currently, there are no biomarker methods for predicting outcome from immunotherapy. Here, we obtained transcriptomic information from a total of 105 baseline tumor samples comprising two cohorts of patients with advanced melanoma treated with programmed cell death protein 1 (PD-1)-based immunotherapies. Gene expression profiles were correlated with progression-free survival (PFS) within consecutive clinical benefit intervals (i.e., 6, 12, 18, and 24 months). Elastic net binomial regression models with cross validation were utilized to compare the predictive value of distinct genes across time. Lasso regression was used to generate a signature predicting long-term benefit (LTB), defined as patients who remain alive and free of disease progression at 24 months post treatment initiation. We show that baseline gene expression profiles were consistently able to predict long-term immunotherapy outcomes with high accuracy. The predictive value of different genes fluctuated across consecutive clinical benefit intervals, with a distinct set of genes defining benefit at 24 months compared to earlier outcomes. A 12-gene signature was able to predict LTB following anti-PD-1 therapy with an area under the curve (AUC) equal to 0.92 and 0.74 in the training and validation set, respectively. Evaluation of LTB, via a unique signature may complement objective response classification and characterize the logistics of sustained antitumor immune responses. 

/pdf/baseline-gene-expression-profiling-determines-long-term-2ei92v70.pdf

Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade

Description A new biography seeks to reacquaint readers with the once widely celebrated scientist A new biography seeks to reacquaint readers with the once widely celebrated scientist

Mark Gerstein

Papers

Design Issues in Implementing a Portable Sample Tracking and Analysis Research Support (STARS) System for PCR Based Microarray Research

Sequences and topology Editorial overview

Compression-based Network Interpretability Schemes

Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade

The lasting legacy of John von Neumann