Data Analysis

Data volumes generated by next-generation sequencing (NGS) technologies is now a major concern for both data storage and transmission. This triggered the need for more efficient methods than general purpose compression tools, such as the widely used gzip method. Researchers ...

SOLiD DNA sequences are typically analyzed using a reference genome, while they are not recommended for de novo assembly of genomes or transcriptomes. This is mainly due to the difficulty in translating the SOLiD color-space data into normal base-space sequences. ...

Next generation sequencing methods, such as RNA-seq, have permitted the exploration of gene expression in a range of organisms which have been studied in ecological contexts but lack a sequenced genome. However, the efficacy and accuracy of RNA-seq annotation methods ...

RNA-seq has been widely used for genome-wide expression profiling. RNA-seq data typically consists of tens of millions of short sequenced reads from different transcripts. However, due to sequence similarity among genes and among isoforms, the source of a given read ...

Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse biological processes and diseases. However, the combinatorial effects of these molecules in a specific biological function are poorly understood. Identifying co-expressed protein-coding genes of lncRNAs would provide ample insight ...

from: https://en.wiki2.org/wiki/List_of_RNA-Seq_bioinformatics_tools 1 Quality control and pre-processing data 1.1 Quality control and filtering data 1.2 Detection of chimeric reads 1.3 Errors Correction 1.4 Pre-processing data 2 Alignment Tools 2.1 Short (Unspliced) aligners 2.2 Spliced aligners 2.2.1 Aligners based on known splice junctions (annotation-guided aligners) 2.2.2 De novo Splice Aligners 2.2.2.1 De novo Splice Aligners that also use annotation optionally 2.2.2.2 Other Spliced Aligners 3 Normalization, Quantitative analysis and Differential Expression 3.1 Multi-tool solutions 4 Workbench (analysis pipeline / integrated solutions) 4.1 Commercial Solutions 4.2 Open (free) Source Solutions 5 Alternative Splicing Analysis 5.1 General Tools 5.2 Intron Retention Analysis 6 Bias Correction 7 Fusion genes/chimeras/translocation finders/structural variations 8 Copy Number Variation identification 9 RNA-Seq simulators 10 Transcriptome assemblers 10.1 Genome-Guided assemblers 10.2 Genome-Independent (de novo) assemblers 10.2.1 Assembly evaluation tools 11 Co-expression networks 12 miRNA prediction 13 Visualization tools 14 Functional, Network & Pathway Analysis Tools 15 Further annotation tools for RNA-Seq data 16 RNA-Seq Databases 17 Webinars and Presentations ...

Advances in transcriptome sequencing (RNA-Seq) have revolutionized the way to characterize and quantify transcripts. The breakthroughs in RNA-Seq technologies give rise to the ever-increasing volumes of data, making data processing the bottleneck of transcriptome research. It becomes crucial to develop ...

from RPubs – by Prasanth A S In order to explain the different methods of normalization and their problems with large array of data, which are assumed to have: most genes are not differentially expressed across conditions. the distribution of ...

Allele-specific sequencing reads provide a powerful signal for identifying molecular quantitative trait loci (QTLs), but they are challenging to analyze and are prone to technical artifacts. Here researchers from the University of Chicago and Stanford University describe WASP, a suite ...

Transcriptome sequencing (RNA-seq) is gradually replacing microarrays for high-throughput studies of gene expression. The main challenge of analyzing microarray data is not in finding differentially expressed genes, but in gaining insights into the biological processes underlying phenotypic differences. To interpret ...