<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36950?offset=200 https://bioinformaticsonline.com/bookmarks/view/37962/wtdbg2-a-de-novo-sequence-assembler-for-long-noisy-reads-produced-by-pacbio-or-oxford-nanopore Fri, 19 Oct 2018 08:48:43 -0500 https://bioinformaticsonline.com/bookmarks/view/37962/wtdbg2-a-de-novo-sequence-assembler-for-long-noisy-reads-produced-by-pacbio-or-oxford-nanopore <![CDATA[Wtdbg2: a de novo sequence assembler for long noisy reads produced by PacBio or Oxford Nanopore]]> Wtdbg2 is a de novo sequence assembler for long noisy reads produced by PacBio or Oxford Nanopore Technologies (ONT). It assembles raw reads without error correction and then builds the consensus from intermediate assembly output. Wtdbg2 is able to assemble the human and even the 32Gb Axolotl genome at a speed tens of times faster than CANU and FALCONwhile producing contigs of comparable base accuracy.

Address of the bookmark: https://github.com/ruanjue/wtdbg2

]]> Neel https://bioinformaticsonline.com/bookmarks/view/40516/nextdenovo-string-graph-based-de-novo-assembler-for-tgs-long-reads Sun, 05 Jan 2020 04:08:29 -0600 https://bioinformaticsonline.com/bookmarks/view/40516/nextdenovo-string-graph-based-de-novo-assembler-for-tgs-long-reads <![CDATA[NextDenovo: string graph-based de novo assembler for TGS long reads]]> NextDenovo is a string graph-based de novo assembler for TGS long reads. It uses a "correct-then-assemble" strategy similar to canu, but requires significantly less computing resources and storages. After assembly, the per-base error rate is about 97-98%, to further improve single base accuracy, please use NextPolish.

NextDenovo contains two core modules: NextCorrect and NextGraph. NextCorrect can be used to correct TGS long reads with approximately 15% sequencing errors, and NextGraph can be used to construct a string graph with corrected reads. It also contains a modified version of minimap2 for adapting input and output and producing more sensitive and accurate dovetail overlaps, and some useful utilities (see here for more details).

Address of the bookmark: https://github.com/Nextomics/NextDenovo

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43384/lncpipea-nextflow-based-pipeline-for-comprehensive-analyses-of-long-non-coding-rnas-from-rna-seq-datasets Fri, 17 Sep 2021 01:57:02 -0500 https://bioinformaticsonline.com/bookmarks/view/43384/lncpipea-nextflow-based-pipeline-for-comprehensive-analyses-of-long-non-coding-rnas-from-rna-seq-datasets <![CDATA[LncPipe:A Nextflow-based pipeline for comprehensive analyses of long non-coding RNAs from RNA-seq datasets]]> The pipeline was developed based on a popular workflow framework Nextflow, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report. LncPipe allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.

Ref https://www.lncrnablog.com/lncpipe-a-nextflow-based-pipeline-for-identification-and-analysis-of-long-non-coding-rnas-from-rna-seq-data/

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Address of the bookmark: https://github.com/likelet/LncPipe

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/38563/hecil-a-hybrid-error-correction-algorithm-for-long-reads-with-iterative-learning Tue, 01 Jan 2019 12:01:00 -0600 https://bioinformaticsonline.com/bookmarks/view/38563/hecil-a-hybrid-error-correction-algorithm-for-long-reads-with-iterative-learning <![CDATA[HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning]]> HECIL—Hybrid Error Correction with Iterative Learning—a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments. 

HECIL’s core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.

Address of the bookmark: https://github.com/NDBL/HECIL

]]> Abhimanyu Singh https://bioinformaticsonline.com/news/view/34711/1mb-long-dna-with-nanopore-technology Tue, 19 Dec 2017 18:49:28 -0600 https://bioinformaticsonline.com/news/view/34711/1mb-long-dna-with-nanopore-technology <![CDATA[1mb long DNA with Nanopore technology]]> The first continuous DNA read of more than a million bases (>1Mb) has been achieved, using Oxford Nanopore sequencing technology. Congratulations to Martin Smith and collaborators! Read more: http://bit.ly/2j5TNCO

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43048/coverm-read-coverage-calculator-for-metagenomics Thu, 29 Apr 2021 23:39:14 -0500 https://bioinformaticsonline.com/bookmarks/view/43048/coverm-read-coverage-calculator-for-metagenomics <![CDATA[CoverM: Read coverage calculator for metagenomics]]> CoverM aims to be a configurable, easy to use and fast DNA read coverage and relative abundance calculator focused on metagenomics applications.

CoverM calculates coverage of genomes/MAGs coverm genome (help) or individual contigs coverm contig (help). Calculating coverage by read mapping, its input can either be BAM files sorted by reference, or raw reads and reference genomes in various formats.

Address of the bookmark: https://github.com/wwood/CoverM

]]> Neel https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus Fri, 05 Jan 2018 04:12:20 -0600 https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus <![CDATA[proovread : large-scale high-accuracy PacBio correction through iterative short read consensus]]> proovread : large-scale high-accuracy PacBio correction through iterative short read consensus

proovread maps high coverage data to pacbio reads (bwa mem, blasr, daligner) in multiple iterations.

Address of the bookmark: https://github.com/BioInf-Wuerzburg/proovread

]]> Jit https://bioinformaticsonline.com/bookmarks/view/39190/chipulate-a-python3-framework-to-simulate-read-counts-in-a-chip-seq-experiment Mon, 25 Mar 2019 12:46:47 -0500 https://bioinformaticsonline.com/bookmarks/view/39190/chipulate-a-python3-framework-to-simulate-read-counts-in-a-chip-seq-experiment <![CDATA[ChIPulate: A Python3 framework to simulate read counts in a ChIP-seq experiment]]> ChIP-seq simulation pipeline, ChIPulate, we assess the impact of various biological and experimental sources of variation on several outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of replicates needed to confidently infer binding strength.

Address of the bookmark: https://github.com/vishakad/chipulate

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/44329/metabuli-%EB%B6%84%EB%A6%AC-improves-metagenomic-read-classification Sat, 03 Jun 2023 20:15:04 -0500 https://bioinformaticsonline.com/bookmarks/view/44329/metabuli-%EB%B6%84%EB%A6%AC-improves-metagenomic-read-classification <![CDATA[Metabuli 분리 improves metagenomic read classification]]> Metabuli 분리 improves metagenomic read classification through metamers, DNA-AA k-mers, to be sensitive and specific, recovering 99% and 98% of DNA or AA classifiers.

 

Metabuli is metagenomic classifier that jointly analyze both DNA and amino acid (AA) sequences. DNA-based classifiers can make specific classifications, exploiting point mutations to distinguish close taxa. AA-based classifiers have higher sensitivity in detecting homology between query and reference sequences, leverageing higher conservation of AA sequences. Metabuli combines the information of both sequence types using a novel k-mer structure, metamer, to enable both specific and sensitive characterization of metagenomic samples. In addition, it can classify reads against a database of any size as long as it fits in the hard disk.

Address of the bookmark: https://github.com/steineggerlab/Metabuli

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/26252/recombination-detection-tool Tue, 02 Feb 2016 10:11:14 -0600 https://bioinformaticsonline.com/bookmarks/view/26252/recombination-detection-tool <![CDATA[Recombination detection tool]]> A program to detect recombination hotspots using population genetic data.

More at https://github.com/auton1/LDhot

Address of the bookmark: https://github.com/auton1/LDhot

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