<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/37962?offset=70 https://bioinformaticsonline.com/bookmarks/view/37457/nanofilt-filtering-and-trimming-of-long-read-sequencing-data Mon, 30 Jul 2018 12:01:52 -0500 https://bioinformaticsonline.com/bookmarks/view/37457/nanofilt-filtering-and-trimming-of-long-read-sequencing-data <![CDATA[nanofilt: Filtering and trimming of long read sequencing data]]> Filtering on quality and/or read length, and optional trimming after passing filters.
Reads from stdin, writes to stdout.

Intended to be used:

  • directly after fastq extraction
  • prior to mapping
  • in a stream between extraction and mapping

https://github.com/wdecoster/nanofilt

Address of the bookmark: https://github.com/wdecoster/nanofilt

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods Wed, 22 Aug 2018 11:05:04 -0500 https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods <![CDATA[LRCstats: a tool for evaluating long reads correction methods]]> LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.

Address of the bookmark: https://github.com/cchauve/lrcstats

]]> Aaryan Lokwani https://bioinformaticsonline.com/bookmarks/view/38012/cosine-non-seeding-method-for-mapping-long-noisy-sequences Fri, 26 Oct 2018 00:41:59 -0500 https://bioinformaticsonline.com/bookmarks/view/38012/cosine-non-seeding-method-for-mapping-long-noisy-sequences <![CDATA[COSINE: non-seeding method for mapping long noisy sequences]]> Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors.

Address of the bookmark: https://github.com/SUwonglab/COSINE

]]> Jit https://bioinformaticsonline.com/bookmarks/view/31139/pbsuite-software-for-long-read-sequencing-data-from-pacbio Mon, 27 Feb 2017 09:54:47 -0600 https://bioinformaticsonline.com/bookmarks/view/31139/pbsuite-software-for-long-read-sequencing-data-from-pacbio <![CDATA[PBSuite: Software for Long-Read Sequencing Data from PacBio]]> PBJelly - the genome upgrading tool. 
PBHoney - the structural variation discovery tool 

Both are contained within the PBSuite code found in downloads.

----- PBJelly -----
Read The Paper 
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0047768

PBJelly is a highly automated pipeline that aligns long sequencing reads (such as PacBio RS reads or long 454 reads in fasta format) to high-confidence draft assembles. PBJelly fills or reduces as many captured gaps as possible to produce upgraded draft genomes. 

----- PBHoney -----
Read The Paper
http://www.biomedcentral.com/1471-2105/15/180/abstract

PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.

Address of the bookmark: https://sourceforge.net/projects/pb-jelly/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37645/lsc-improving-pacbio-long-read-accuracy-by-short-read-alignment Thu, 06 Sep 2018 16:27:35 -0500 https://bioinformaticsonline.com/bookmarks/view/37645/lsc-improving-pacbio-long-read-accuracy-by-short-read-alignment <![CDATA[LSC: Improving PacBio Long Read Accuracy by Short Read Alignment]]>
  • Added Command line argument support.
  • Multi-stage execution modes.
  • Support for parallelization. Now execution proceeds in batches of long reads the size of which can be set by --long_read_batch_size N.
  • Better compressed intermediate files.
  • Added utilities folder.
  • Added support for multiple short read files.
  • Removed use of configuration file.

    • Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/

    ]]>     Abhimanyu Singh     https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data Tue, 22 Jan 2019 06:26:55 -0600 https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data <![CDATA[Katuali is a flexible consensus pipeline implemented in Snakemake to basecall, assemble, and polish Oxford Nanopore Technologies' sequencing data]]>
  • Run a pipeline processing fast5s to a consensus in a single command.
  • Recommended fixed "standard" and "fast" pipelines.
  • Interchange basecaller, assembler, and consensus components of the pipelines simply by changing the target filepath.
  • Seemless distribution of tasks over local or distributed compute.
  • Highly configurable.
  • Open source (Mozilla Public License 2.0).

    • Documentation can be found at https://nanoporetech.github.io/katuali/.

    Address of the bookmark: https://github.com/nanoporetech/katuali

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/31568/pacbio-long-reads-compatible-software-and-tools Wed, 15 Mar 2017 14:19:01 -0500 https://bioinformaticsonline.com/bookmarks/view/31568/pacbio-long-reads-compatible-software-and-tools <![CDATA[Pacbio Long Reads Compatible Software and Tools]]> The following software packages are known to be compatible with PacBio® data, in addition to PacBio's own SMRT® Analysis suite. All packages are believed to be open source or freely available for non-commercial use. See the individual project sites for up-to-date license information. A separate page lists commercial software.

    Know of any other open source software for PacBio data? Email us.

    Software categories:

    Address of the bookmark: https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software

    ]]>     Archana Malhotra     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

    ]]>     Neel     https://bioinformaticsonline.com/bookmarks/view/36597/gappadder-a-sensitive-approach-for-closing-gaps-on-draft-genomes-with-short-sequence-reads Mon, 14 May 2018 05:25:48 -0500 https://bioinformaticsonline.com/bookmarks/view/36597/gappadder-a-sensitive-approach-for-closing-gaps-on-draft-genomes-with-short-sequence-reads <![CDATA[GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads]]> This software is provided ``as is” without warranty of any kind. In no event shall the author be held responsible for any damage resulting from the use of this software. The program package, including source codes, executables, and this documentation, is distributed free of charge. If you use this program in a publication, please cite the following reference:
    Chong Chu, Xin Li, and Yufeng Wu. "GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads." bioRxiv (2017): 125534.

    Address of the bookmark: https://github.com/Reedwarbler/GAPPadder

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/40531/shasta-long-read-assembler Tue, 14 Jan 2020 06:47:07 -0600 https://bioinformaticsonline.com/bookmarks/view/40531/shasta-long-read-assembler <![CDATA[Shasta long read assembler]]> The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by Oxford Nanopore flow cells.

    Computational methods used by the Shasta assembler include:

    • Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
    • Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

    More at https://chanzuckerberg.github.io/shasta/index.html

    Address of the bookmark: https://github.com/chanzuckerberg/shasta

    ]]>     Jit