<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/42619?offset=160 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/36026/mmseqs20-ultra-fast-and-sensitive-protein-search-and-clustering-suite Thu, 22 Mar 2018 10:40:51 -0500 https://bioinformaticsonline.com/bookmarks/view/36026/mmseqs20-ultra-fast-and-sensitive-protein-search-and-clustering-suite <![CDATA[MMseqs2.0: ultra fast and sensitive protein search and clustering suite]]> MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.

The MMseqs2 user guide is available as Github Wiki or as PDF file (Thanks to pandoc!)

Please cite: Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, doi: 10.1038/nbt.3988 (2017).

Address of the bookmark: https://github.com/soedinglab/MMseqs2

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43439/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite Wed, 06 Oct 2021 07:01:14 -0500 https://bioinformaticsonline.com/bookmarks/view/43439/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite <![CDATA[MMseqs2: ultra fast and sensitive sequence search and clustering suite]]> MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.

Address of the bookmark: https://github.com/soedinglab/MMseqs2

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