<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36597?offset=700 https://bioinformaticsonline.com/bookmarks/view/39872/miropeats-discovers-regions-of-sequence-similarity-amongst-any-set-of-dna-sequences Mon, 26 Aug 2019 17:55:24 -0500 https://bioinformaticsonline.com/bookmarks/view/39872/miropeats-discovers-regions-of-sequence-similarity-amongst-any-set-of-dna-sequences <![CDATA[Miropeats: discovers regions of sequence similarity amongst any set of DNA sequences]]> Miropeats discovers regions of sequence similarity amongst any set of DNA sequences and then presents this similarity information graphically. Sequence similarity searching is a very general tool that forms the basis of many different biological sequence analyses but it is limited by the verbosity of traditional alignment presentation styles. Miropeats enhances the utility of conventional DNA sequence comparisons when looking at long lengths of sequence similarity by summarizing extensive large scale sequence similarities on a single page of graphics. The latest version of Miropeats can be used as a general pairwise alignment program or in its traditional role sorting out a big mess of overlapping or similar regions.

Address of the bookmark: http://www.littlest.co.uk/software/bioinf/old_packages/miropeats/

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/40510/reps-repeat-masked-phrap-with-scaffolding-a-wgs-sequence-assembler Sat, 04 Jan 2020 01:08:09 -0600 https://bioinformaticsonline.com/bookmarks/view/40510/reps-repeat-masked-phrap-with-scaffolding-a-wgs-sequence-assembler <![CDATA[RePS: Repeat-masked Phrap with scaffolding, a WGS sequence assembler]]> RePS (Repeat-masked Phrap with scaffolding), a WGS sequence assembler, that explicitly identifies exact kmer repeats from the shotgun data and removes them prior to the assembly. The established software Phrap is used to compute meaningful error probabilities for each base. Clone-end-pairing information is used to construct scaffolds that order and orient the contigs. The updated version of RePS incorporates some of the ideas introduced by Phusion on clustering

image

More at

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC186573/

Address of the bookmark: ftp://ftp.genomics.org.cn/pub/ricedb/Tools/RePS/RePS-IBM-AIX.tar.gz

]]> Jit https://bioinformaticsonline.com/bookmarks/view/41146/lofreq-a-sequence-quality-aware-ultra-sensitive-variant-caller-for-ngs-data Tue, 18 Feb 2020 03:24:22 -0600 https://bioinformaticsonline.com/bookmarks/view/41146/lofreq-a-sequence-quality-aware-ultra-sensitive-variant-caller-for-ngs-data <![CDATA[LoFreq*: A sequence-quality aware, ultra-sensitive variant caller for NGS data]]> LoFreq* (i.e. LoFreq version 2) is a fast and sensitive variant-caller for inferring SNVs and indels from next-generation sequencing data. It makes full use of base-call qualities and other sources of errors inherent in sequencing (e.g. mapping or base/indel alignment uncertainty), which are usually ignored by other methods or only used for filtering.

https://github.com/CSB5/lofreq

http://csb5.github.io/lofreq/installation/

https://github.com/CSB5/lofreq/tree/master/dist

Address of the bookmark: http://csb5.github.io/lofreq/

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/43268/kmer-a-suite-of-tools-for-dna-sequence-analysis Wed, 18 Aug 2021 00:02:54 -0500 https://bioinformaticsonline.com/bookmarks/view/43268/kmer-a-suite-of-tools-for-dna-sequence-analysis <![CDATA[Kmer: a suite of tools for DNA sequence analysis]]> More at https://help.rc.ufl.edu/doc/Kmer

This also includes:

  • A2Amapper: ATAC, Assembly to Assembly Comparision tool:
    • Comparative mapping between two genome assemblies (same species), or between two different genomes (cross species).
  • Sim4db:
    • Spliced alignment of cDNA and genomic sequences, from the same (sim4) or related (sim4cc) species. Optimized for high-throughput batched alignment.
  • LEAFF:
    • LEAFF (ahem, Let's Extract Anything From Fasta) is a utility program for working with multi-fasta files. In addition to providing random access to the base level, it includes several analysis functions.
  • Meryl:
    • An out-of-core k-mer counter. The amount of sequence that can be processed for any size k depends only on the amount of free disk space.

Address of the bookmark: https://help.rc.ufl.edu/doc/Kmer

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite Wed, 03 Jan 2018 05:05:46 -0600 https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite <![CDATA[Seal: SEquence ALignment evaluation suite]]> Seal is a comprehensive sequencing simulation and alignment tool evaluation suite. This software (implemented in Java) provides several utilities that can be used to evaluate alignment algorithms, including:

  • Reading a pre-existing reference genome from one or more FASTA files.
  • Alternatively, generating an artificial reference genome based on input parameters (length, repeat count, repeat length, repeat variability rate).
  • Simulating reads from random locations in the genome based on input parameters of read length, coverage, sequencing error rate, and indel rate.
  • Applying alignment tools to the genome and the reads through a standardized interface.
  • Parsing the output of the alignment tool and calculating the number of reads that were correctly or incorrectly mapped.
  • Computing run times and measures of accuracy.

Seal has interfaces to evaluate the following software packages:

  • Bowtie
  • BWA
  • MAQ
  • mrFAST
  • mrsFAST
  • Novoalign
  • SHRiMP
  • SOAPv2

Address of the bookmark: http://compbio.case.edu/seal/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/39104/hipstr-haplotype-inference-and-phasing-for-short-tandem-repeats Thu, 07 Mar 2019 21:13:06 -0600 https://bioinformaticsonline.com/bookmarks/view/39104/hipstr-haplotype-inference-and-phasing-for-short-tandem-repeats <![CDATA[HipSTR: Haplotype inference and phasing for Short Tandem Repeats]]> HipSTR was specifically developed to deal with these errors in the hopes of obtaining more robust STR genotypes. In particular, it accomplishes this by:

  1. Learning locus-specific PCR stutter models using an EM algorithm
  2. Mining candidate STR alleles from population-scale sequencing data
  3. Employing a specialized hidden Markov model to align reads to candidate alleles while accounting for STR artifacts
  4. Utilizing phased SNP haplotypes to genotype and phase STRs

Address of the bookmark: https://github.com/tfwillems/HipSTR

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