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DESeq2 is a software package in the field of bioinformatics and computational biology for the statistical programming language R. It is primarily employed for the analysis of high-throughput RNA sequencing (RNA-seq) data to identify differentially expressed genes between different experimental conditions.
Within computational biology, an MA plot is an application of a Bland–Altman plot for visual representation of genomic data. The plot visualizes the differences between measurements taken in two samples, by transforming the data onto M (log ratio) and A (mean average) scales, then plotting these values.
fastqp Simple FASTQ quality assessment using Python. Kraken: [9] A set of tools for quality control and analysis of high-throughput sequence data. HTSeq [10] The Python script htseq-qa takes a file with sequencing reads (either raw or aligned reads) and produces a PDF file with useful plots to assess the technical quality of a run.
A full summary of canonicalization changes is listed below: The document is encoded in UTF-8; Line breaks normalized to #xA on input, before parsing; Attribute values are normalized, as if by a validating processor; Character and parsed entity references are replaced; CDATA sections are replaced with their character content
FORM has been the essential tool to calculate the higher-order QCD beta function. The mathematical structure of multiple zeta values has been researched with dedicated FORM programs. [2] The software package FormCalc which is widely used in the physics community to calculate Feynman diagrams is built on top of FORM.
pyOpenMS is an open-source Python library for mass spectrometry, specifically for the analysis of proteomics and metabolomics data in Python. Peaksel Proprietary: This web-based (available both in cloud as SaaS and as on-prem installation) software for LC/MS data processing supports batch processing and high-throughput experiments.
In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series. Some ...
8: thickEnd: End coordinates from which the annotation is no longer displayed in a thicker way on a graphical representation (e.g.: the stop codon of a gene) No 9: itemRgb: RGB value in the form R, G, B (e.g. 255,0,0) determining the display color of the annotation contained in the BED file No 10: blockCount