Bioconductor

Bioconductor is a free, open source and open development software project for the analysis and comprehension of genomic data generated by wet lab experiments in molecular biology. Bioconductor is based primarily on the statistical R programming language, but does contain contributions in other programming languages. It has two releases each year that follow the semiannual releases of R. At any one time there is a release version, which corresponds to the released version of R, and a development version, which corresponds to the development version of R. Most users will find the release version appropriate for their needs. In addition there are many genome annotation packages available that are mainly, but not solely, oriented towards different types of microarrays. The project was started in the Fall of 2001 and is overseen by the Bioconductor core team, based primarily at the Fred Hutchinson Cancer Research Center, with other members coming from international institutions.

Bioconductor is a free, open source and open development software project for the analysis and comprehension of genomic data generated by wet lab experiments in molecular biology. Bioconductor is based primarily on the statistical R programming language, but does contain contributions in other programming languages. It has two releases each year that follow the semiannual releases of R. At any one time there is a release version, which corresponds to the released version of R, and a development version, which corresponds to the development version of R. Most users will find the release version appropriate for their needs. In addition there are many genome annotation packages available that are mainly, but not solely, oriented towards different types of microarrays. The project was started in the Fall of 2001 and is overseen by the Bioconductor core team, based primarily at the Fred Hutchinson Cancer Research Center, with other members coming from international institutions. ==Application of Bioconductor in small-RNA seq and microRNA data analysis== ===Introduction=== Small RNA sequencing is a widely used technique to study microRNA(miRNAs), small interfering RNAs (siRNAs), piwi-interacting RNA (piRNAs) that play a crucial role in RNA-mediated gene silencing process or known as RNA silencing /Gene silencing process. RNA silencing process employs different types of substrates which give rise to different types of RNA population, namely microRNAs, siRNAs, etc. In the laboratory, small RNA sequencing typically start by extraction of RNA from cells or tissues, followed by Adapter ligation to the 5' and 3' ends of small RNAs, followed by Reverse transcription and PCR amplification to generate cDNA libraries. Finally, High-throughput sequencing ( most commonly Illumina platforms) is used to produce millions of short reads. These resulting data then undergo computational processing to align reads to reference genomes of particular species or miRNA databases. ===Bioconductor in RNA Biology=== Bioconductor(BioC) is a widely used open-source platform for analysing different types of small-RNA sequencing and genomic data. It primarily utilizes the R programming language and offers a wide range of packages for bioinformatics and computational biology. Bioconductor provides a wide range of packages for handling small-RNA seq data among them few are widely used by researchers. Popular Bioconductor packages like DESeq2, edgeR, limma + voom, GenomicAlignment, GenomicFeatures, Rsubread, ShortRead, featureCounts provide robust analysis of RNA-seq data. ====DESeq2==== It uses a negative binomial distribution modeling for differential expression analysis of read count from RNA-seq data. It is popular for dispersion estimation, normalization, and visualization by PCA plots or MA plots. ====edgeR==== It also uses a negative binomial distribution modeling for differential expression analysis of read count from RNA seq data. In contrast with DESeq2, it is used when sample number is relatively small. ====limma + voom==== It is used to estimate the mean-variance relationship of count data and transforms it to log2-counts per million (CPM). It is used for analysing microarray data and also to calculate CPM value from RNA-seq data. ====GenomicAlignment==== It is widely popular for reads like BAM and SAM file alignment to assign aligned reads to genes or miRNAs for downstream analysis. ====GenomicFeatures==== It is used to build transcript-centric annotation databases like TxDb objects which store information about genes, exons, transcripts from GTF/GFF files. ====Rsubread==== It is used mostly for summarizing the reads and mapping, where functions like align(), featureCounts() are used to provide an efficient alternative to external aligners like STAR or HISAT2. ===ShortRead=== It is often used to pre-process the raw FASTQ files to check the quality of raw FASTQ data, which comes from a sequencing platform like Illumina sequencing etc. ===Computational Workflow=== ====Data Import and Quality Control==== FASTQ files are typically imported by using different Bioconductor packages like ShortRead which provides quality assessment reports. ====Adapter Trimming and Filtering==== Different external tools like Cutadapt, trimmomatic is used to remove the adapter sequence from the raw FASTQ files. This helps to improve the Reads quality. ====Read Alignment==== The processed Reads are aligned to the reference genome. This alignment can be done by different aligners like Rsubread, or external tools such as STAR, with results stored in standard formats like SAM (Sequence Alignment Map) or BAM (Binary Alignment Map) files . ====Annotation of microRNAs==== Bioconductor supports to integrate miRBase data where different packages like miRBaseConverter, AnnotationHub, org.Mm.eg.db are used for annotate reads to known miRNAs. ====Quantification==== Count reads are mapped to known genes or microRNAs, and summarize counts across samples. ====Differential Expression Analysis==== After mapping and quantifying microRNA expression, different well-established packages like DESeq2, edgeR are used for differential expression analysis. ====Visualization==== To interpret and present miRNA expression results, different visualization packages are used like ggplot2, pheatmap, ComplexHeatmap which generates Volcano plot (statistics), PCA plot (Principal component analysis), MA plot, pheatmap are used to visualize the differential expression data. ==See also== Computational biology Bioinformatics List of open source bioinformatics software List of sequence alignment software R (programming language) DNA microarray Affymetrix, a microarray technology platform ==References== ==External links== The R Project GNU R is a programming language for statistical computing. Bioconductor Releases The community of the Debian GNU/Linux distribution strives towards an automated building of BioConductor packages for their distribution. BioKnoppix and Quantian are projects extending Knoppix that have contributed bootable Debian GNU/Linux CDs providing BioConductor installations.

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