<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/42826?offset=250 https://bioinformaticsonline.com/bookmarks/view/42581/autogluon-automl-for-text-image-and-tabular-data Thu, 07 Jan 2021 05:33:17 -0600 https://bioinformaticsonline.com/bookmarks/view/42581/autogluon-automl-for-text-image-and-tabular-data <![CDATA[AutoGluon: AutoML for Text, Image, and Tabular Data]]> AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on text, image, and tabular data.

Address of the bookmark: https://github.com/awslabs/autogluon

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44742/nasa-open-science-data-repository Wed, 18 Dec 2024 11:54:47 -0600 https://bioinformaticsonline.com/bookmarks/view/44742/nasa-open-science-data-repository <![CDATA[NASA Open Science Data Repository]]> The NASA Open Science Data Repository (OSDR) enables access to space-related data from experiments and missions that investigate biological and health responses of terrestrial life to spaceflight. The goal of OSDR is to enable multi-modal and multi-hierarchical fundamental space life science data be reused toward basic science, applied science, and operational outcomes for space exploration and knowledge discovery. These data include ‘omics, phenotypic, physiological, behavioral, hardware, environmental telemetry; raw, processed; tabular, text, code, bioimaging, and video.

https://www.nasa.gov/reference/osdr-data-processing/

Address of the bookmark: https://www.nasa.gov/osdr/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/27076/ale-a-generic-assembly-likelihood-evaluation-framework-for-assessing-the-accuracy-of-genome-and-metagenome-assemblies Tue, 26 Apr 2016 03:38:43 -0500 https://bioinformaticsonline.com/bookmarks/view/27076/ale-a-generic-assembly-likelihood-evaluation-framework-for-assessing-the-accuracy-of-genome-and-metagenome-assemblies <![CDATA[ALE: a Generic Assembly Likelihood Evaluation Framework for Assessing the Accuracy of Genome and Metagenome Assemblies]]> Assembly Likelihood Evaluation (ALE) framework that overcomes these limitations, systematically evaluating the accuracy of an assembly in a reference-independent manner using rigorous statistical methods. This framework is comprehensive, and integrates read quality, mate pair orientation and insert length (for paired-end reads), sequencing coverage, read alignment and k-mer frequency. ALE pinpoints synthetic errors in both single and metagenomic assemblies, including single-base errors, insertions/deletions, genome rearrangements and chimeric assemblies presented in metagenomes. At the genome level with real-world data, ALE identifies three large misassemblies from the Spirochaeta smaragdinae finished genome, which were all independently validated by Pacific Biosciences sequencing. At the single-base level with Illumina data, ALE recovers 215 of 222 (97%) single nucleotide variants in a training set from a GC-rich Rhodobacter sphaeroides genome. Using real Pacific Biosciences data, ALE identifies 12 of 12 synthetic errors in a Lambda Phage genome, surpassing even Pacific Biosciences' own variant caller, EviCons. In summary, the ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process.

More at http://www.ncbi.nlm.nih.gov/pubmed/23303509

Address of the bookmark: http://sc932.github.io/ALE/about.html

]]> Neel https://bioinformaticsonline.com/bookmarks/view/42303/fqc-dashboard-integrates-fastqc-results-into-a-web-based-interactive-and-extensible-fastq-quality-control-tool Tue, 10 Nov 2020 01:30:22 -0600 https://bioinformaticsonline.com/bookmarks/view/42303/fqc-dashboard-integrates-fastqc-results-into-a-web-based-interactive-and-extensible-fastq-quality-control-tool <![CDATA[FQC Dashboard: Integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool]]> FQC is software that facilitates quality control of FASTQ files by carrying out a QC protocol using FastQC, parsing results, and aggregating quality metrics into an interactive dashboard designed to richly summarize individual sequencing runs. The dashboard groups samples in dropdowns for navigation among the data sets, utilizes human-readable configuration files to manipulate the pages and tabs, and is extensible with CSV data.

Address of the bookmark: https://github.com/pnnl/fqc

]]> Shruti Paniwala https://bioinformaticsonline.com/bookmarks/view/37239/kat-a-k-mer-analysis-toolkit-to-quality-control-ngs-datasets-and-genome-assemblies Fri, 06 Jul 2018 03:36:45 -0500 https://bioinformaticsonline.com/bookmarks/view/37239/kat-a-k-mer-analysis-toolkit-to-quality-control-ngs-datasets-and-genome-assemblies <![CDATA[KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies]]> KAT is a suite of tools that analyse jellyfish hashes or sequence files (fasta or fastq) using kmer counts. The following tools are currently available in KAT:

  • hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in output for easy plotting.
  • gcp: K-mer GC Processor. Creates a matrix of the number of K-mers found given a GC count and a K-mer count.
  • comp: K-mer comparison tool. Creates a matrix of shared K-mers between two (or three) sequence files or hashes.
  • sect: SEquence Coverage estimator Tool. Estimates the coverage of each sequence in a file using K-mers from another sequence file.
  • blob: Given, reads and an assembly, calculates both the read and assembly K-mer coverage along with GC% for each sequence in the assembly.SEquence Coverage estimator Tool.
  • filter: Filtering tools. Contains tools for filtering k-mer hashes and FastQ/A files:
    • kmer: Produces a k-mer hash containing only k-mers within specified coverage and GC tolerances.
    • seq: Filters a sequence file based on whether or not the sequences contain k-mers within a provided hash.
  • plot: Plotting tools. Contains several plotting tools to visualise K-mer and compare distributions. The following plot tools are available:
    • density: Creates a density plot from a matrix created with the "comp" tool. Typically this is used to compare two K-mer hashes produced by different NGS reads.
    • profile: Creates a K-mer coverage plot for a single sequence. Takes in fasta coverage output coverage from the "sect" tool
    • spectra-cn: Creates a stacked histogram using a matrix created with the "comp" tool. Typically this is used to compare a jellyfish hash produced from a read set to a jellyfish hash produced from an assembly. The plot shows the amount of distinct K-mers absent, as well as the copy number variation present within the assembly.
    • spectra-hist: Creates a K-mer spectra plot for a set of K-mer histograms produced either by jellyfish-histo or kat-histo.
    • spectra-mx: Creates a K-mer spectra plot for a set of K-mer histograms that are derived from selected rows or columns in a matrix produced by the "comp".

In addition, KAT contains a python script for analysing the mathematical distributions present in the K-mer spectra in order to determine how much content is present in each peak.

This README only contains some brief details of how to install and use KAT. For more extensive documentation please visit: https://kat.readthedocs.org/en/latest/

https://academic.oup.com/bioinformatics/article/33/4/574/2664339 

Address of the bookmark: https://github.com/TGAC/KAT

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