<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/35033?offset=50 https://bioinformaticsonline.com/bookmarks/view/41459/jcvipython-utility-libraries-on-genome-assembly-annotation-and-comparative-genomics Tue, 17 Mar 2020 06:19:06 -0500 https://bioinformaticsonline.com/bookmarks/view/41459/jcvipython-utility-libraries-on-genome-assembly-annotation-and-comparative-genomics <![CDATA[JCVI:Python utility libraries on genome assembly, annotation and comparative genomics]]> Collection of Python libraries to parse bioinformatics files, or perform computation related to assembly, annotation, and comparative genomics.

https://github.com/tanghaibao/jcvi

More at https://github.com/tanghaibao/jcvi/wiki

Address of the bookmark: https://github.com/tanghaibao/jcvi

]]> Jit https://bioinformaticsonline.com/bookmarks/view/41559/dahak-benchmarking-and-containerization-of-tools-for-analysis-of-complex-non-clinical-metagenomes Thu, 09 Apr 2020 04:56:09 -0500 https://bioinformaticsonline.com/bookmarks/view/41559/dahak-benchmarking-and-containerization-of-tools-for-analysis-of-complex-non-clinical-metagenomes <![CDATA[Dahak: benchmarking and containerization of tools for analysis of complex non-clinical metagenomes.]]> Dahak is a software suite that integrates state-of-the-art open source tools for metagenomic analyses. Tools in the dahak software suite will perform various steps in metagenomic analysis workflows including data pre-processing, metagenome assembly, taxonomic and functional classification, genome binning, and gene assignment. We aim to deliver the analytical framework as a robust and reliable containerized workflow system, which will be free from dependency, installation, and execution problems typically associated with other open-source bioinformatics solutions. This will maximize the transparency, data provenance (i.e., the process of tracing the origins of data and its movement through the workflow), and reproducibility.

More at https://dahak-metagenomics.github.io/dahak/

Address of the bookmark: https://github.com/dahak-metagenomics/dahak

]]> BioStar 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/pages/view/43977/read-simulators Fri, 30 Sep 2022 06:48:18 -0500 https://bioinformaticsonline.com/pages/view/43977/read-simulators <![CDATA[Read Simulators]]> Short Read Simulators

With the popularity of next-generation sequencing (NGS) technologies, many NGS read simulators have been developed. Currently, many of the popular short read simulators are designed to simulate reads mimicking many Illumina, 454 and SOLiD platforms. Listed below are some popular short read simulators. Links to their publications are provided as well.

  1. MetaSim
  2. wgsim
  3. SimNGS
  4. ArtificialFastqGenerator
  5. InSilicoSeq

Long Read Simulators

With the advancements in sequencing technologies, scientists have shown an increasing interest in using third-generation sequencing (TGS) technologies. Currently, many of the popular long read simulators are designed to simulate reads mimicking the two main TGS technologies; (1) Pacific Biosciences (PacBio) and (2) Oxford Nanopore (ONT). Listed below are some of the popular and recently introduced PacBio and ONT simulators. Links to their publications are provided as well.

PacBio Simulators

  1. PBSIM
  2. LongISLND
  3. SimLoRD
  4. NPBSS
  5. PaSS

ONT Simulators

  1. NanoSim
  2. Nanopore SimulatION
  3. DeepSimulator
  4. DeepSimulator1.5
]]> Abhi 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/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads Wed, 22 Aug 2018 10:40:27 -0500 https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads <![CDATA[SimLoRD: A read simulator for third generation sequencing reads]]> SimLoRD is a read simulator for third generation sequencing reads and is currently focused on the Pacific Biosciences SMRT error model.

Reads are simulated from both strands of a provided or randomly generated reference sequence.

  • The reference can be read from a FASTA file or randomly generated with a given GC content. It can consist of several chromosomes, whose structure is respected when drawing reads. (Simulation of genome rearrangements may be incorporated at a later stage.)
  • The read lengths can be determined in four ways: drawing from a log-normal distribution (typical for genomic DNA), sampling from an existing FASTQ file (typical for RNA), sampling from a a text file with integers (RNA), or using a fixed length
  • Quality values and number of passes depend on fragment length.
  • Provided subread error probabilities are modified according to number of passes
  • Outputs reads in FASTQ format and alignments in SAM format

Address of the bookmark: https://bitbucket.org/genomeinformatics/simlord/

]]> Aaryan Lokwani 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 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/27110/easyfig Fri, 29 Apr 2016 05:49:39 -0500 https://bioinformaticsonline.com/bookmarks/view/27110/easyfig <![CDATA[Easyfig]]> Easyfig has moved to github, for newer releases of Easyfig please visit our new webpage - https://mjsull.github.io/Easyfig.  Easyfig is a Python application for creating linear comparison figures of multiple genomic loci with an easy-to-use graphical user interface (GUI).

More at http://easyfig.sourceforge.net/

Address of the bookmark: http://easyfig.sourceforge.net/

]]> Poonam Mahapatra https://bioinformaticsonline.com/news/view/27311/release-notes-for-genome-workbench-2105 Thu, 12 May 2016 13:49:41 -0500 https://bioinformaticsonline.com/news/view/27311/release-notes-for-genome-workbench-2105 <![CDATA[Release Notes for Genome Workbench 2.10.5]]> New Features in latest release

  • New ProSplign tool integrated with Genome Workbench (Tutorial, Video)
  • New export function for BAM/cSRA coverage graphs (Tutorial)
  • New export function for alignments GFF3 format ((Tutorial))
  • Tree View: implemented new export mode based on selections (tutorial coming)
  • Tree View: added support for distance based circular trees
  • Tree View: new rooting mode (Midpoint Root) results in more balanced trees (Tutorial)
  • Tree View: added possibility to right-click on an edge between two nodes and "Place Root at Middle of Branch" – to re-root at mid-branch (Tutorial)
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