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            "name": "iBLAST",
            "description": "iBLAST is tool which performs incremental BLAST of new sequences via automated e-value correction.",
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                    "uri": "http://edamontology.org/topic_3071",
                    "term": "Biological databases"
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                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
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                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
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                {
                    "doi": "10.1371/JOURNAL.PONE.0249410",
                    "pmid": "33886589",
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                    "metadata": {
                        "title": "iBLAST: Incremental BLAST of new sequences via automated e-value correction",
                        "abstract": "© 2021 Dash et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Search results from local alignment search tools use statistical scores that are sensitive to the size of the database to report the quality of the result. For example, NCBI BLAST reports the best matches using similarity scores and expect values (i.e., e-values) calculated against the database size. Given the astronomical growth in genomics data throughout a genomic research investigation, sequence databases grow as new sequences are continuously being added to these databases. As a consequence, the results (e.g., best hits) and associated statistics (e.g., e-values) for a specific set of queries may change over the course of a genomic investigation. Thus, to update the results of a previously conducted BLAST search to find the best matches on an updated database, scientists must currently rerun the BLAST search against the entire updated database, which translates into irrecoverable and, in turn, wasted execution time, money, and computational resources. To address this issue, we devise a novel and efficient method to redeem past BLAST searches by introducing iBLAST. iBLAST leverages previous BLAST search results to conduct the same query search but only on the incremental (i.e., newly added) part of the database, recomputes the associated critical statistics such as e-values, and combines these results to produce updated search results. Our experimental results and fidelity analyses show that iBLAST delivers search results that are identical to NCBI BLAST at a substantially reduced computational cost, i.e., iBLAST performs (1 + δ)/δ times faster than NCBI BLAST, where δ represents the fraction of database growth. We then present three different use cases to demonstrate that iBLAST can enable efficient biological discovery at a much faster speed with a substantially reduced computational cost.",
                        "date": "2021-04-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Dash S."
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                            {
                                "name": "Rahman S.R."
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                            {
                                "name": "Hines H.M."
                            },
                            {
                                "name": "Feng W.-C."
                            }
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                        "journal": "PLoS ONE"
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                {
                    "name": "Sajal Dash",
                    "email": "dashs@ornl.gov",
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                    "name": "Wu-chun Feng",
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            "name": "BLAST-QC",
            "description": "BLAST-QC is a tool for automated analysis of BLAST results.  It is a quality control filter and parser for NCBI BLAST XML results.",
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                            "term": "Parsing"
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                            "term": "Local alignment"
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                    "uri": "http://edamontology.org/topic_0769",
                    "term": "Workflows"
                },
                {
                    "uri": "http://edamontology.org/topic_0091",
                    "term": "Bioinformatics"
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                    "uri": "http://edamontology.org/topic_3372",
                    "term": "Software engineering"
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                "C",
                "Java",
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                    "doi": "10.1186/S40793-020-00361-Y",
                    "pmid": "33902722",
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                        "title": "BLAST-QC: Automated analysis of BLAST results",
                        "abstract": "© 2020 The Author(s).Background: The Basic Local Alignment Search Tool (BLAST) from NCBI is the preferred utility for sequence alignment and identification for bioinformatics and genomics research. Among researchers using NCBI's BLAST software, it is well known that analyzing the results of a large BLAST search can be tedious and time-consuming. Furthermore, with the recent discussions over the effects of parameters such as '-max_target_seqs' on the BLAST heuristic search process, the use of these search options are questionable. This leaves using a stand-alone parser as one of the only options of condensing these large datasets, and with few available for download online, the task is left to the researcher to create a specialized piece of software anytime they need to analyze BLAST results. The need for a streamlined and fast script that solves these issues and can be easily implemented into a variety of bioinformatics and genomics workflows was the initial motivation for developing this software. Results: In this study, we demonstrate the effectiveness of BLAST-QC for analysis of BLAST results and its desirability over the other available options. Applying genetic sequence data from our bioinformatic workflows, we establish BLAST_QC's superior runtime when compared to existing parsers developed with commonly used BioPerl and BioPython modules, as well as C and Java implementations of the BLAST_QC program. We discuss the 'max_target_seqs' parameter, the usage of and controversy around the use of the parameter, and offer a solution by demonstrating the ability of our software to provide the functionality this parameter was assumed to produce, as well as a variety of other parsing options. Executions of the script on example datasets are given, demonstrating the implemented functionality and providing test-cases of the program. BLAST-QC is designed to be integrated into existing software, and we establish its effectiveness as a module of workflows or other processes. Conclusions: BLAST-QC provides the community with a simple, lightweight and portable Python script that allows for easy quality control of BLAST results while avoiding the drawbacks of other options. This includes the uncertain results of applying the -max_target_seqs parameter or relying on the cumbersome dependencies of other options like BioPerl, Java, etc. which add complexity and run time when running large data sets of sequences. BLAST-QC is ideal for use in high-throughput workflows and pipelines common in bioinformatic and genomic research, and the script has been designed for portability and easy integration into whatever type of processes the user may be running.",
                        "date": "2020-08-12T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Torkian B."
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                            {
                                "name": "Hann S."
                            },
                            {
                                "name": "Preisner E."
                            },
                            {
                                "name": "Norman R.S."
                            }
                        ],
                        "journal": "Environmental Microbiomes"
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                    "name": "R. Sean Norman",
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            "name": "BlastKOALA",
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                {
                    "uri": "http://edamontology.org/topic_3174",
                    "term": "Metagenomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
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                    "url": "https://www.kegg.jp/blastkoala/help_blastkoala.html",
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                {
                    "doi": "10.1016/j.jmb.2015.11.006",
                    "pmid": "26585406",
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                    "metadata": {
                        "title": "BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences",
                        "abstract": "© 2015 The Authors.BlastKOALA and GhostKOALA are automatic annotation servers for genome and metagenome sequences, which perform KO (KEGG Orthology) assignments to characterize individual gene functions and reconstruct KEGG pathways, BRITE hierarchies and KEGG modules to infer high-level functions of the organism or the ecosystem. Both servers are made freely available at the KEGG Web site (http://www.kegg.jp/blastkoala/). In BlastKOALA, the KO assignment is performed by a modified version of the internally used KOALA algorithm after the BLAST search against a non-redundant dataset of pangenome sequences at the species, genus or family level, which is generated from the KEGG GENES database by retaining the KO content of each taxonomic category. In GhostKOALA, which utilizes more rapid GHOSTX for database search and is suitable for metagenome annotation, the pangenome dataset is supplemented with Cd-hit clusters including those for viral genes. The result files may be downloaded and manipulated for further KEGG Mapper analysis, such as comparative pathway analysis using multiple BlastKOALA results.",
                        "date": "2016-02-22T00:00:00Z",
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                                "name": "Kanehisa M."
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                            {
                                "name": "Sato Y."
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                        "journal": "Journal of Molecular Biology"
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                        "title": "Incorporating sequence quality data into alignment improves DNA read mapping",
                        "abstract": "New DNA sequencing technologies have achieved breakthroughs in throughput, at the expense of higher error rates. The primary way of interpreting biological sequences is via alignment, but standard alignment methods assume the sequences are accurate. Here, we describe how to incorporate the per-base error probabilities reported by sequencers into alignment. Unlike existing tools for DNA read mapping, our method models both sequencer errors and real sequence differences. This approach consistently improves mapping accuracy, even when the rate of real sequence difference is only 0.2%. Furthermore, when mapping Drosophila melanogaster reads to the Drosophila simulans genome, it increased the amount of correctly mapped reads from 49 to 66%. This approach enables more effective use of DNA reads from organisms that lack reference genomes, are extinct or are highly polymorphic. © The Author(s) 2010. Published by Oxford University Press.",
                        "date": "2010-01-27T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Frith M.C."
                            },
                            {
                                "name": "Wan R."
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                            {
                                "name": "Horton P."
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                        "journal": "Nucleic Acids Research"
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                        "title": "BLAT - The BLAST-like alignment tool",
                        "abstract": "Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments. A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences. BLAT's speed stems from an index of all nonoverlapping K-mers in the genome. This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly. BLAT has several major stages. It uses the index to find regions in the genome likely to be homologous to the query sequence. It performs an alignment between homologous regions. It stitches together these aligned regions (often exons) into larger alignments (typically genes). Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible. This paper describes how BLAT was optimized. Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches. BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications. http://genome.ucsc.edu hosts a web-based BLAT server for the human genome.",
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                    "term": "DNA"
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                    "url": "https://sourceforge.net/p/bait/wiki/Home/",
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            "publication": [
                {
                    "doi": "10.1038/nmeth.2206",
                    "pmid": "23042453",
                    "pmcid": "PMC3580294",
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                        "title": "DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution",
                        "abstract": "DNA rearrangements such as sister chromatid exchanges (SCEs) are sensitive indicators of genomic stress and instability, but they are typically masked by single-cell sequencing techniques. We developed Strand-seq to independently sequence parental DNA template strands from single cells, making it possible to map SCEs at orders-of-magnitude greater resolution than was previously possible. On average, murine embryonic stem (mES) cells exhibit eight SCEs, which are detected at a resolution of up to 23 bp. Strikingly, Strand-seq of 62 single mES cells predicts that the mm9 mouse reference genome assembly contains at least 17 incorrectly oriented segments totaling nearly 1% of the genome. These misoriented contigs and fragments have persisted through several iterations of the mouse reference genome and have been difficult to detect using conventional sequencing techniques. The ability to map SCE events at high resolution and fine-tune reference genomes by Strand-seq dramatically expands the scope of single-cell sequencing. © 2012 Nature America, Inc. All rights reserved.",
                        "date": "2012-11-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Falconer E."
                            },
                            {
                                "name": "Hills M."
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                            {
                                "name": "Naumann U."
                            },
                            {
                                "name": "Poon S.S.S."
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                            {
                                "name": "Chavez E.A."
                            },
                            {
                                "name": "Sanders A.D."
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                            {
                                "name": "Zhao Y."
                            },
                            {
                                "name": "Hirst M."
                            },
                            {
                                "name": "Lansdorp P.M."
                            }
                        ],
                        "journal": "Nature Methods"
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        {
            "name": "BAlaS",
            "description": "BAlaS is an interactive web application for performing CASM via BudeAlaScan and visualizing its results. BAlaS is interactive and intuitive to use. Results are displayed directly in the browser for the structure being interrogated enabling their rapid inspection. BAlaS has broad applications in areas, such as drug discovery and protein-interface design.",
            "homepage": "https://balas.app",
            "biotoolsID": "balas",
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                            "term": "Protein structure validation"
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                            "uri": "http://edamontology.org/operation_0248",
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                    ],
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            "topic": [
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                    "uri": "http://edamontology.org/topic_0128",
                    "term": "Protein interactions"
                },
                {
                    "uri": "http://edamontology.org/topic_3957",
                    "term": "Protein interaction experiment"
                },
                {
                    "uri": "http://edamontology.org/topic_0130",
                    "term": "Protein folding, stability and design"
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                    "uri": "http://edamontology.org/topic_3336",
                    "term": "Drug discovery"
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            "operatingSystem": [],
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                "Elm",
                "Python",
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                    "doi": "10.1093/BIOINFORMATICS/BTAA026",
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                    "metadata": {
                        "title": "BAlaS: Fast, interactive and accessible computational alanine-scanning using BudeAlaScan",
                        "abstract": "© 2020 The Author(s). Published by Oxford University Press. All rights reserved.Motivation: In experimental protein engineering, alanine-scanning mutagenesis involves the replacement of selected residues with alanine to determine the energetic contribution of each side chain to forming an interaction. For example, it is often used to study protein-protein interactions. However, such experiments can be time-consuming and costly, which has led to the development of programmes for performing computational alanine-scanning mutagenesis (CASM) to guide experiments. While programmes are available for this, there is a need for a real-time web application that is accessible to non-expert users. Results: Here, we present BAlaS, an interactive web application for performing CASM via BudeAlaScan and visualizing its results. BAlaS is interactive and intuitive to use. Results are displayed directly in the browser for the structure being interrogated enabling their rapid inspection. BAlaS has broad applications in areas, such as drug discovery and protein-interface design.",
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                        "authors": [
                            {
                                "name": "Wood C.W."
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                            {
                                "name": "Ibarra A.A."
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                            {
                                "name": "Bartlett G.J."
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                            {
                                "name": "Wilson A.J."
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                            {
                                "name": "Wilson A.J."
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                            {
                                "name": "Woolfson D.N."
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                            {
                                "name": "Woolfson D.N."
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                            {
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                            {
                                "name": "Sessions R.B."
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                            {
                                "name": "Sessions R.B."
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                        "journal": "Bioinformatics"
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                            "term": "Phasing"
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                    "term": "Gene expression"
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                    "term": "DNA polymorphism"
                },
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                    "uri": "http://edamontology.org/topic_3295",
                    "term": "Epigenetics"
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                    "term": "Gene transcripts"
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                {
                    "doi": "10.1093/BIOINFORMATICS/BTAA636",
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                    "note": null,
                    "metadata": {
                        "title": "BYASE: A Python library for estimating gene and isoform level allele-specific expression",
                        "abstract": "© 2020 Oxford University Press. All rights reserved.Allele-specific expression (ASE) is involved in many important biological mechanisms. We present a python package BYASE and its graphical user interface (GUI) tool BYASE-GUI for the identification of ASE from singleend and paired-end RNA-seq data based on Bayesian inference, which can simultaneously report differences in gene-level and isoform-level expression. BYASE uses both phased SNPs and non-phased SNPs, and supports polyploid organisms. Availability and implementation: The source codes of BYASE and BYASE-GUI are freely available at https://github. com/ncjllld/byase and https://github.com/ncjllld/byase_gui.",
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