<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/37561?offset=30 https://bioinformaticsonline.com/bookmarks/view/44171/hairsplitter-assembling-long-reads-in-an-unknown-number-of-haplotypes Wed, 07 Dec 2022 00:13:40 -0600 https://bioinformaticsonline.com/bookmarks/view/44171/hairsplitter-assembling-long-reads-in-an-unknown-number-of-haplotypes <![CDATA[HairSplitter: assembling long reads in an unknown number of haplotypes]]> Pros and cons of HairSplitter Limitations of HairSplitter:

Not very fast: it re-polishes the whole assembly 

Limited in the number of haplotypes

Strengths of HairSplitter:

Very modular, can be used with any assembler

Naive: makes no assumption on ploidy, parameter-free

Safe: won’t artificially duplicate contigs

 

HairSplitter splits collapsed assemblies from “draft” assemblies obtained by any means

HairSplitter can recover haplotypes and distinguish repeated elements

Only needs sequencing reads, potentially error-prone

HairSplitter splits collapsed assemblies from “draft” assemblies obtained by any means

HairSplitter can recover haplotypes and distinguish repeated elements

Only needs sequencing reads, potentially error-prone

Not really available yet (github.com/RolandFaure/HairSplitter)

https://hal.archives-ouvertes.fr/hal-03864075/file/RolandFaure_presentation_SeqBIM_2022.pdf

Address of the bookmark: https://hal.archives-ouvertes.fr/hal-03817928/document

]]> BioStar 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/36812/porechop-tool-for-finding-and-removing-adapters-from-oxford-nanopore-reads Tue, 29 May 2018 07:33:44 -0500 https://bioinformaticsonline.com/bookmarks/view/36812/porechop-tool-for-finding-and-removing-adapters-from-oxford-nanopore-reads <![CDATA[Porechop: tool for finding and removing adapters from Oxford Nanopore reads]]> Porechop is a tool for finding and removing adapters from Oxford Nanopore reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity.

Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

Address of the bookmark: https://github.com/rrwick/Porechop

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/31105/understanding-pacbio Fri, 24 Feb 2017 10:17:36 -0600 https://bioinformaticsonline.com/bookmarks/view/31105/understanding-pacbio <![CDATA[Understanding PacBio]]> This tutorial includes resources for learning more about PacBio data and bioinformatics analysis, and includes content suitable for both beginners and experts. Below are links to training modules (webinars and PowerPoint presentations) to help you get started with your data processing, as well as information for specialized applications.

Training Resources:

Specialized Applications:

Address of the bookmark: https://github.com/PacificBiosciences/Bioinformatics-Training/wiki

]]> Jitendra Narayan 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/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/36758/pbalign-maps-pacbio-reads-to-reference-sequences-and-saves-alignments-to-a-bam-file Thu, 24 May 2018 10:06:52 -0500 https://bioinformaticsonline.com/bookmarks/view/36758/pbalign-maps-pacbio-reads-to-reference-sequences-and-saves-alignments-to-a-bam-file <![CDATA[pbalign: maps PacBio reads to reference sequences and saves alignments to a BAM file]]> Address of the bookmark: https://github.com/PacificBiosciences/pbalign

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r Fri, 13 Aug 2021 07:44:05 -0500 https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r <![CDATA[QuasR: Quantification and annotation of short reads in R]]> The QuasR package (short for Quantify and annotate short reads in R) integrates the functionality of several R packages (such as IRanges (Lawrence et al. 2013) and Rsamtools) and external software (e.g. bowtie, through the Rbowtie package, and HISAT2, through the Rhisat2 package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single R script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html

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