different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure.

multiGSEA package

he active participation of RBPs in regulated gene expressionmay be pertinent to an emerging concept of phase separationduring gene expression

Orb has a clear preference for canonical TTTT(A)1–3T

Orb and Orb2 Bind to Linear CPE Motifs.

We identified 3,128 targets for Orb and 2,154 for Orb2

thresholds might be transferred wrongly. To prevent this, flowLearn selects prototypes that define sample groups

flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry

We then devised a measure of co-transcriptional splicing for each exon by calculating the difference of intronic read coverage at the 3′ and 5′ ends of the flanking introns

Nascent transcripts at different stages of formation throughout the intron generate a gradient. The fact that this slope spans across individual introns, rather than across the entire transcript, can be explained by co-transcriptional splicing.

Intronic RNAs were abundant in brain tissue, particularly for genes involved in axonal growth and synaptic transmission

Finally, the fact that many IRTs are subject to NMD adds an additional layer of complexity, as the gene may be observed as expressed at a lower level due to the specific degradation of IRTs

s many of these harbour independently transcribed small RNAs, such as snoRNAs or microRNAs (Fig. 3C). If host transcripts are expressed at high levels, and a coverage cut-off is not implemented or used, it might be erroneously concluded that IR affects the coding gene.

implement a coverage cut-off,

only ∼10–25% of sequencing reads in a typical paired-end library span one or more splice junctions

Pitfalls of splice-junction-only approaches to analyse IR from short read sequencing data

Hence, any bioinformatic tool for IR identification and differential analysis which does not include an approach to deal with multi-mapping reads and repetitive sequences in the genome is likely to miss substantial, functionally relevant biological complexity.

filters in place to discard reads that map to more loci than a specific threshold

Long read sequencing captures full intron length but is limited by sequencing depth and accuracy

GSEA

based on the orthology relationship or conservation of their flanking exons

Recently, enrichment of particular RNA binding sites has been observed in sequences of frequently retained introns as well as their flanking exons in human

benchmarking studies of best practices for IR detection, normalisation and quantitation similar to those available for differential gene expression analysis [50,51] need to be carried out.

Both strategies also require a filtering step to segregate IR in differentially expressed genes from IR in non-differentially expressed genes

followed by a robust assessment of whether the read depth is adequate to sample intronic regions. This can be done by subsampling reads and examining splice junction or intron coverage statistics

validation rates for estimates in the levels of IR remain low. For example, one study reported a correlation coefficient (r) of only 0.63 between IR fold-changes determined using RNA-seq and qRT-PCR [19]

IR detection can be confounded by antisense transcription

passing any coverage, splice junction boundary balance or other cut-off.

or uniquely mappable region of the intron

coverage-based approaches

Depending on the statistical tests implemented by each tool, this may lead to biased and unreliable analyses.

improvement in alternative splicing assessment when read depth is increased to 200–300 million reads per sample [19,43].

recommend at least 70 million reads per sample

FPKM of constitutive and alternative splicing event were calculated by sum of the corresponding isoforms (constitutive or alternative splicing event may have multiple isoforms). Then, fisher’s exact test was applied to above FPKM to analyze differential alternative splicing between well-watered and salt-stress treatments

machine learning to derive a computational model that takes as input DNA sequences and applies general rules to predict splicing in human tissues

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(Unix-alikes.) system(cmd, input = ) now uses ‘shell-execution-environment’ redirection, which will be more natural if cmd is not a single command (but requires a POSIX-compliant shell). (Wish of PR#15508)