<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/43048?offset=50 https://bioinformaticsonline.com/bookmarks/view/42132/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins Mon, 17 Aug 2020 05:25:10 -0500 https://bioinformaticsonline.com/bookmarks/view/42132/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins <![CDATA[SqueezeMeta: a fully automated metagenomics pipeline, from reads to bins]]> SqueezeMeta is a full automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual genomes via binning procedures. Thus, SqueezeMeta features several unique characteristics:

  1. Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome
  2. Co-assembly of a large number of metagenomes via merging of individual metagenomes
  3. Includes binning and bin checking, for retrieving individual genomes
  4. The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.
  5. Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.
  6. Metatranscriptomic support via mapping of cDNA reads against reference metagenomes

Address of the bookmark: https://github.com/jtamames/SqueezeMeta

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/44595/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins Sat, 06 Jul 2024 04:29:16 -0500 https://bioinformaticsonline.com/bookmarks/view/44595/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins <![CDATA[SqueezeMeta: a fully automated metagenomics pipeline, from reads to bins]]> SqueezeMeta is a full automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual genomes via binning procedures. Thus, SqueezeMeta features several unique characteristics:

  1. Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome
  2. Co-assembly of a large number of metagenomes via merging of individual metagenomes
  3. Includes binning and bin checking, for retrieving individual genomes
  4. The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.
  5. Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.
  6. Metatranscriptomic support via mapping of cDNA reads against reference metagenomes

Address of the bookmark: https://github.com/jtamames/SqueezeMeta

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/29487/shinyheatmap Fri, 21 Oct 2016 05:12:11 -0500 https://bioinformaticsonline.com/bookmarks/view/29487/shinyheatmap <![CDATA[Shinyheatmap]]> Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap.

More at http://biorxiv.org/content/early/2016/09/21/076463 

Address of the bookmark: http://shinyheatmap.com/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/31343/metabat-an-efficient-tool-for-accurately-reconstructing-single-genomes-from-complex-microbial-communities Mon, 06 Mar 2017 03:44:34 -0600 https://bioinformaticsonline.com/bookmarks/view/31343/metabat-an-efficient-tool-for-accurately-reconstructing-single-genomes-from-complex-microbial-communities <![CDATA[MetaBAT: An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities]]> MetaBAT, An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities

Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed an automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. Tested on both synthetic and real metagenome datasets, MetaBAT outperforms alternative methods in both accuracy and computational efficiency. Applying MetaBAT to an assembly from 1,704 human gut samples formed 1,634 genome bins (>200kb) in 3 hours, where 621 genome bins are >50% complete with <5% contamination from other species. Further analysis shows that the quality of these genome bins approaches manually curated genomes.

Address of the bookmark: https://bitbucket.org/berkeleylab/metabat

]]> Jit https://bioinformaticsonline.com/bookmarks/view/32905/bigmac-breaking-inaccurate-genomes-and-merging-assembled-contigs-for-long-read-metagenomic-assembly Mon, 22 May 2017 05:43:51 -0500 https://bioinformaticsonline.com/bookmarks/view/32905/bigmac-breaking-inaccurate-genomes-and-merging-assembled-contigs-for-long-read-metagenomic-assembly <![CDATA[BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly]]> This tool is for users to upgrade their metagenomics assemblies using long reads. This includes fixing mis-assemblies and scaffolding/gap-filling. If you encounter any issues, please contact me at kklam@eecs.berkeley.edu. My name is Ka-Kit Lam.

https://github.com/kakitone/MetaFinisherSC

https://github.com/kakitone/BIGMAC

Address of the bookmark: https://github.com/kakitone/BIGMAC

]]> Jit https://bioinformaticsonline.com/bookmarks/view/35057/ectools-long-read-correction-and-other-correction-tools Fri, 05 Jan 2018 04:02:22 -0600 https://bioinformaticsonline.com/bookmarks/view/35057/ectools-long-read-correction-and-other-correction-tools <![CDATA[ECTOOLS: Long Read Correction and other Correction tools]]> Long Read Correction and other Correction tools

This package is a loose collection of scripts. To run the correction
routine see the section below. Descriptions of the other scripts
are at the bottom of this file.

Contact: gurtowsk@cshl.edu

In short, the correction algorithm takes as input the unitigs from a short read assembly and uses them to correct long read data. More background information for the algorithm can be found:
http://schatzlab.cshl.edu/presentations/2013-06-18.PBUserMeeting.pdf

Address of the bookmark: https://github.com/jgurtowski/ectools

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data Tue, 27 Nov 2018 04:43:57 -0600 https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data <![CDATA[lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data]]> lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.

lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.

 

Address of the bookmark: https://github.com/vpc-ccg/lordfast

]]> BioJoker https://bioinformaticsonline.com/bookmarks/view/34394/tulip-the-uncorrected-long-read-itegration-pipeline Thu, 23 Nov 2017 09:30:01 -0600 https://bioinformaticsonline.com/bookmarks/view/34394/tulip-the-uncorrected-long-read-itegration-pipeline <![CDATA[TULIP - The Uncorrected Long read Itegration Pipeline]]> #Running TULIP (The Uncorrected Long-read Integration Process), version 0.4 late 2016 (European eel)

TULIP currently consists of to Perl scripts, tulipseed.perl and tulipbulb.perl. These are very much intended as prototypes, and additional components and/or implementations are likely to follow. 
Tulipseed takes as input alignments files of long reads to sparse short seeds, and outputs a graph and scaffold structures. Tulipbulb adds long read sequencing data to these.

 

https://github.com/Generade-nl/TULIP

Address of the bookmark: https://github.com/Generade-nl/TULIP

]]> Jit https://bioinformaticsonline.com/bookmarks/view/36632/tulip-the-uncorrected-long-read-integration-pipeline Tue, 15 May 2018 09:06:37 -0500 https://bioinformaticsonline.com/bookmarks/view/36632/tulip-the-uncorrected-long-read-integration-pipeline <![CDATA[TULIP - The Uncorrected Long read Integration Pipeline]]> Address of the bookmark: https://github.com/Generade-nl/TULIP

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool Thu, 02 Aug 2018 07:39:46 -0500 https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool <![CDATA[LSC :a long read error correction tool]]> Getting Started

These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.

  • Read the LSC_requirements for running LSC.
  • Download and set-up the LSC package.
  • Follow the tutorial to see how LSC works on some example data.
  • Read the manual if anything is unclear.
  • You're ready, Happy LSCing!

Latest publication

Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong 
Improving PacBio Long Read Accuracy by Short Read Alignment [Manuscript] 
PLoS ONE 2012. 7(10): e46679. doi:10.1371/journal.pone.0046679

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

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