<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/34618?offset=30 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/37561/hercules-a-profile-hmm-based-hybrid-error-correction-algorithm-for-long-reads Mon, 20 Aug 2018 14:14:11 -0500 https://bioinformaticsonline.com/bookmarks/view/37561/hercules-a-profile-hmm-based-hybrid-error-correction-algorithm-for-long-reads <![CDATA[Hercules: a profile HMM-based hybrid error correction algorithm for long reads]]> Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time- efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Results: We designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platformtextquoterights error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads. Availability:

Hercules source code is available at https://github.com/BilkentCompGen/Hercules

Address of the bookmark: https://github.com/BilkentCompGen/Hercules

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37776/rhat-a-seed-and-extension-based-noisy-long-read-alignment-tool Sun, 23 Sep 2018 05:12:22 -0500 https://bioinformaticsonline.com/bookmarks/view/37776/rhat-a-seed-and-extension-based-noisy-long-read-alignment-tool <![CDATA[rHAT: a seed-and-extension-based noisy long read alignment tool]]> rHAT is a seed-and-extension-based noisy long read alignment tool. It is suitable for aligning 3rd generation sequencing reads which are in large read length with relatively high error rate, especially Pacbio's Single Molecule Read-time (SMRT) sequencing reads.

Address of the bookmark: https://github.com/dfguan/rHAT

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore Mon, 12 Nov 2018 05:26:48 -0600 https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore <![CDATA[Pacasus: Correction of palindromes in long reads from PacBio and Nanopore]]>
Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. Check the poster from the Revolutionizing Next-Generation Sequencing (2nd edition) conference in the source folder: https://github.com/swarris/Pacasus/blob/master/vib2017.pdf.

The prepint version is found on http://www.biorxiv.org/content/early/2017/08/09/173872

It uses the pyPaSWAS framework for sequence alignment (https://github.com/swarris/pyPaSWAS)

Address of the bookmark: https://github.com/swarris/Pacasus

]]> BioStar 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/36861/eagler-a-scaffolding-tool-for-long-reads Mon, 04 Jun 2018 05:26:03 -0500 https://bioinformaticsonline.com/bookmarks/view/36861/eagler-a-scaffolding-tool-for-long-reads <![CDATA[EAGLER: a scaffolding tool for long reads.]]> EAGLER is a scaffolding tool for long reads. The scaffolder takes as input a draft genome created by any NGS assembler and a set of long reads. The long reads are used to extend the contigs present in the NGS draft and possibly join overlapping contigs. EAGLER supports both PacBio and Oxford Nanopore reads.

The tool should be compatible with most UNIX flavors and has been successfully tested on the following operating systems:

  • Mac OS X 10.11.1
  • Mac OS X 10.10.3
  • Ubuntu 14.04 LTS
https://bib.irb.hr/datoteka/844447.Diplomski_2015_Luka_terbi.pdf

Address of the bookmark: https://github.com/mculinovic/EAGLER

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38892/wtdbg2-a-fuzzy-bruijn-graph-approach-to-long-noisy-reads-assembly Mon, 04 Feb 2019 04:53:47 -0600 https://bioinformaticsonline.com/bookmarks/view/38892/wtdbg2-a-fuzzy-bruijn-graph-approach-to-long-noisy-reads-assembly <![CDATA[wtdbg2: A fuzzy Bruijn graph approach to long noisy reads assembly]]> 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 -x rs -g 4.6m -t 16 -i reads.fa.gz -fo prefix
./wtpoa-cns -t 16 -i prefix.ctg.lay.gz -fo prefix.ctg.fa

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

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/36478/the-marvel-assembler Fri, 04 May 2018 19:18:41 -0500 https://bioinformaticsonline.com/bookmarks/view/36478/the-marvel-assembler <![CDATA[The MARVEL assembler]]> MARVEL consists of a set of tools that facilitate the overlapping, patching, correction and assembly of noisy (not so noisy ones as well) long reads.

The assembly process can be summarized as follows:

  1. overlap
  2. patch reads
  3. overlap (again)
  4. scrubbing
  5. assembly graph construction and touring
  6. optional read correction
  7. fasta file creation

Address of the bookmark: https://github.com/schloi/MARVEL

]]> Jit https://bioinformaticsonline.com/bookmarks/view/40208/ragoo-fast-reference-guided-scaffolding-of-genome-assembly-contigs Sun, 27 Oct 2019 00:57:23 -0500 https://bioinformaticsonline.com/bookmarks/view/40208/ragoo-fast-reference-guided-scaffolding-of-genome-assembly-contigs <![CDATA[RaGOO: Fast Reference-Guided Scaffolding of Genome Assembly Contigs]]> Alonge M, Soyk S, Ramakrishnan S, Wang X, Goodwin S, Sedlazeck FJ, Lippman ZB, Schatz MC: Fast and accurate reference-guided scaffolding of draft genomes. bioRxiv 2019.

RaGOO is a tool for coalescing genome assembly contigs into pseudochromosomes via minimap2 alignments to a closely related reference genome. The focus of this tool is on practicality and therefore has the following features:

  1. Good performance. On a MacBook Pro using Arabidopsis data, pseudochromosome construction takes less than a minute and the whole pipeline with SV calling takes ~2 minutes.
  2. Intact ordering and orienting of contigs.
  3. Misassembly correction
  4. GFF lift-over
  5. Structural variant calling with and integrated version of Assemblytics
  6. Confidence scores associated with the grouping, localization, and orientation for each contig.

Address of the bookmark: https://github.com/malonge/RaGOO

]]> 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