<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36618?offset=90 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/41501/hicanu-accurate-assembly-of-segmental-duplications-satellites-and-allelic-variants-from-high-fidelity-long-reads Fri, 27 Mar 2020 22:49:31 -0500 https://bioinformaticsonline.com/bookmarks/view/41501/hicanu-accurate-assembly-of-segmental-duplications-satellites-and-allelic-variants-from-high-fidelity-long-reads <![CDATA[HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads]]> HiCanu, a significant modification of the Canu assembler designed to leverage the full potential of HiFi reads via homopolymer compression, overlap-based error correction, and aggressive false overlap filtering. 

More at https://www.biorxiv.org/content/10.1101/2020.03.14.992248v3

Address of the bookmark: https://github.com/marbl/canu

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/31137/finishersc-a-repeat-aware-and-scalable-tool-for-upgrading-de-novo-assembly-using-long-reads Mon, 27 Feb 2017 09:49:45 -0600 https://bioinformaticsonline.com/bookmarks/view/31137/finishersc-a-repeat-aware-and-scalable-tool-for-upgrading-de-novo-assembly-using-long-reads <![CDATA[FinisherSC: a repeat-aware and scalable tool for upgrading de novo assembly using long reads]]> FinisherSC, a repeat-aware and scalable tool for upgrading de novo assembly using long reads. Experiments with real data suggest that FinisherSC can provide longer and higher quality contigs than existing tools while maintaining high concordance.

Address of the bookmark: http://kakitone.github.io/finishingTool/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/32190/dbg2olcefficient-assembly-of-large-genomes-using-long-erroneous-reads-of-the-third-generation-sequencing-technologies Wed, 19 Apr 2017 10:09:51 -0500 https://bioinformaticsonline.com/bookmarks/view/32190/dbg2olcefficient-assembly-of-large-genomes-using-long-erroneous-reads-of-the-third-generation-sequencing-technologies <![CDATA[DBG2OLC:Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies]]> DBG2OLC:Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies

Our work is published in Scientific Reports:

Ye, C. et al. DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies. Sci. Rep. 6, 31900; doi: 10.1038/srep31900 (2016).

http://www.nature.com/articles/srep31900

The manual can be downloaded from:

https://github.com/yechengxi/DBG2OLC/raw/master/Manual.docx

To use precompiled versions,please go to:

https://github.com/yechengxi/DBG2OLC/tree/master/compiled

 

Address of the bookmark: https://github.com/yechengxi/DBG2OLC

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37563/colormap-correcting-long-reads-by-mapping-short-reads Mon, 20 Aug 2018 14:17:05 -0500 https://bioinformaticsonline.com/bookmarks/view/37563/colormap-correcting-long-reads-by-mapping-short-reads <![CDATA[CoLoRMap: Correcting Long Reads by Mapping short reads]]> Second generation sequencing technologies paved the way to an exceptional increase in the number of sequenced genomes, both prokaryotic and eukaryotic. However, short reads are difficult to assemble and often lead to highly fragmented assemblies. The recent developments in long reads sequencing methods offer a promising way to address this issue. However, so far long reads are characterized by a high error rate, and assembling from long reads require a high depth of coverage. This motivates the development of hybrid approaches that leverage the high quality of short reads to correct errors in long reads.We introduce CoLoRMap, a hybrid method for correcting noisy long reads, such as the ones produced by PacBio sequencing technology, using high-quality Illumina paired-end reads mapped onto the long reads. Our algorithm is based on two novel ideas: using a classical shortest path algorithm to find a sequence of overlapping short reads that minimizes the edit score to a long read and extending corrected regions by local assembly of unmapped mates of mapped short reads. Our results on bacterial, fungal and insect data sets show that CoLoRMap compares well with existing hybrid correction methods.The source code of CoLoRMap is freely available for non-commercial use at https://github.com/sfu-compbio/colormap

ehaghshe@sfu.ca or cedric.chauve@sfu.ca

Address of the bookmark: https://github.com/sfu-compbio/colormap

]]> Jit 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/31156/splitbam-splits-a-bam-by-chromosomes Tue, 28 Feb 2017 09:01:28 -0600 https://bioinformaticsonline.com/bookmarks/view/31156/splitbam-splits-a-bam-by-chromosomes <![CDATA[splitbam: splits a BAM by chromosomes]]> splitbam splits a BAM by chromosomes.

Using the reference sequence dictionary (*.dict), it also creates some empty BAM files if no sam record was found for a chromosome. A pair of 'mock' SAM-Records can also be added to those empty BAMs to avoid some tools (like samtools) to crash.

Usage

java -jar splitbam.jar -p OUT/__CHROM__/__CHROM__.bam -R ref.fasta (bam|sam|stdin)

Options

  • -h help; This screen.
  • -R (indexed reference file) REQUIRED.
  • -u (unmapped chromosome name): default:Unmapped
  • -e | --empty : generate EMPTY bams for chromosome having no read mapped
  • -m | --mock : if option '-e', add a mock pair of sam records to the empty bam
  • -p (output file/bam pattern) REQUIRED. MUST contain __CHROM__ and end with .bam
  • -s assume input is sorted.
  • -x | --index create index.
  • -t | --tmp (dir) tmp file directory
  • -G (file) chrom-group file (see below)

Address of the bookmark: https://code.google.com/archive/p/jvarkit/wikis/SplitBam.wiki

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