<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/37205? https://bioinformaticsonline.com/bookmarks/view/34292/automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data Mon, 13 Nov 2017 05:10:23 -0600 https://bioinformaticsonline.com/bookmarks/view/34292/automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data <![CDATA[Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data]]> Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data
AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC results of each fastq file/pair.
Currently it supports processing data from HiSeq 2000/2500/3000/4000, Nextseq 500/550, MiniSeq...and other Illumina 1.8 or newer formats

Address of the bookmark: https://github.com/OpenGene/AfterQC

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/41046/iseqqc-a-tool-for-expression-based-quality-control-in-rna-sequencing Sun, 16 Feb 2020 08:47:17 -0600 https://bioinformaticsonline.com/bookmarks/view/41046/iseqqc-a-tool-for-expression-based-quality-control-in-rna-sequencing <![CDATA[iSeqQC: a tool for expression-based quality control in RNA sequencing]]> iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers.

http://cancerwebpa.jefferson.edu/iSeqQC/

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3399-8

Address of the bookmark: https://github.com/gkumar09/iSeqQC

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/42826/ktrim-an-extra-fast-and-accurate-adapter-and-quality-trimmer-for-sequencing-data Thu, 11 Feb 2021 21:39:05 -0600 https://bioinformaticsonline.com/bookmarks/view/42826/ktrim-an-extra-fast-and-accurate-adapter-and-quality-trimmer-for-sequencing-data <![CDATA[Ktrim: an extra-fast and accurate adapter- and quality-trimmer for sequencing data]]> Ktrim is written in C++ for GNU Linux/Unix platforms. After uncompressing the source package, you can find an executable file ktrim under bin/ directory compiled using g++ v4.8.5 and linked with libz v1.2.7 for Linux x86_64 system. If you could not run it (which is usually caused by low version of libc++ or libz library) or you want to build a version optimized for your system, you can re-compile the programs:

user@linux$ make clean && make

Address of the bookmark: https://github.com/hellosunking/Ktrim

]]> Jit 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

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37602/indexcov-fast-coverage-quality-control-for-whole-genome-sequencing Wed, 29 Aug 2018 09:20:46 -0500 https://bioinformaticsonline.com/bookmarks/view/37602/indexcov-fast-coverage-quality-control-for-whole-genome-sequencing <![CDATA[Indexcov: fast coverage quality control for whole-genome sequencing]]> indexcov, an efficient estimator of whole-genome sequencing coverage to rapidly identify samples with aberrant coverage profiles, reveal large-scale chromosomal anomalies, recognize potential batch effects, and infer the sex of a sample. Indexcov is available at https://github.com/brentp/goleft under the MIT license.

Address of the bookmark: https://github.com/brentp/goleft

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37493/fastq-stats-in-emoji Mon, 06 Aug 2018 10:20:20 -0500 https://bioinformaticsonline.com/bookmarks/view/37493/fastq-stats-in-emoji <![CDATA[Fastq stats in Emoji :)]]> Read one or more FASTQ files, fastqe will compute quality stats for each file and print those stats as emoji... for some reason.

Given a fastq file in Illumina 1.8+/Sanger format, calculate the mean (rounded) score for each position and print a corresponding emoji!

Example

https://fastqe.com/

Address of the bookmark: https://github.com/lonsbio/fastqe

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/41669/filtlong-quality-filtering-tool-for-long-reads Wed, 13 May 2020 10:23:55 -0500 https://bioinformaticsonline.com/bookmarks/view/41669/filtlong-quality-filtering-tool-for-long-reads <![CDATA[Filtlong: quality filtering tool for long reads]]> Filtlong is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter.

Filtlong builds into a stand-alone executable:

git clone https://github.com/rrwick/Filtlong.git
cd Filtlong
make -j
bin/filtlong -h

Address of the bookmark: https://github.com/rrwick/Filtlong

]]> Radha Agarkar https://bioinformaticsonline.com/bookmarks/view/37835/variantbam-filtering-and-profiling-of-next-generational-sequencing-data-using-region-specific-rules Thu, 04 Oct 2018 16:30:44 -0500 https://bioinformaticsonline.com/bookmarks/view/37835/variantbam-filtering-and-profiling-of-next-generational-sequencing-data-using-region-specific-rules <![CDATA[VariantBam: Filtering and profiling of next-generational sequencing data using region-specific rules]]> VariantBam is a tool to extract/count specific sets of sequencing reads from next-generational sequencing files. To save money, disk space and I/O, one may not want to store an entire BAM on disk. In many cases, it would be more efficient to store only those read-pairs or reads who intersect some region around the variant locations. Alternatively, if your scientific question is focused on only one aspect of the data (e.g. breakpoints), many reads can be removed without losing the information relevant to the problem.

 

Address of the bookmark: https://github.com/broadinstitute/VariantBam

]]> Rahul Nayak