Splicing heterogeneity: separating signal from noise

Wan, Yihan; Larson, Daniel R.

doi:10.1186/s13059-018-1467-4

Cited by 61 publications

(41 citation statements)

References 123 publications

(105 reference statements)

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“…While the uncertainty about the number of genes strongly decreased in the past 20 years (compare Figure and Figure ), the number of generated transcripts remains largely unclear (Figure B). It steadily increased in RefSeq, GENCODE, and other databases over the past ten years, from about 60 000 in 2009 to 210 000 in 2018, but it is not clear yet, to which extent these transcripts result from erroneous splicing or are translated to a significant level, if at all …”

Section: Is There Consensus On the Low‐hanging Fruits?mentioning

confidence: 99%

The Protein‐Coding Human Genome: Annotating High‐Hanging Fruits

et al. 2019

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The major transcript variants of human protein-coding genes are annotated to a certain degree of accuracy combining manual curation, transcript data, and proteomics evidence. However, there is considerable disagreement on the annotation of about 2000 genes-they can be protein-coding, noncoding, or pseudogenes-and on the annotation of most of the predicted alternative transcripts. Pure transcriptome mapping approaches seem to be limited in discriminating functional expression from noise. These limitations have partially been overcome by dedicated algorithms to detect alternative spliced micro-exons and wobble splice variants. Recently, knowledge about splice mechanism and protein structure are incorporated into an algorithm to predict neighboring homologous exons, often spliced in a mutually exclusive manner. Predicted exons are evaluated by transcript data, structural compatibility, and evolutionary conservation, revealing hundreds of novel coding exons and splice mechanism re-assignments. The emerging human pan-genome is necessitating distinctive annotations incorporating differences between individuals and between populations.

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Section: Is There Consensus On the Low‐hanging Fruits?mentioning

confidence: 99%

The Protein‐Coding Human Genome: Annotating High‐Hanging Fruits

et al. 2019

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“…While most of current studies quantify variability using number of molecules or number of sequencing reads corresponding to the gene, the structure of the transcript is rarely taken into account. Yet, variability in pre-mRNA maturation is also observed(103). At the splicing level, statistical methods were developed to identify genes with condition-specific splicing ratios(31), while variation in splicing can be defined and quantified using a recently suggested local splicing variation units(100).…”

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confidence: 99%

Gene expression variability: the other dimension in transcriptome analysis

Jong

Moshkin

Guryev

2019

Physiological Genomics

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Transcriptome sequencing is a powerful technique to study molecular changes that underlie the differences in physiological conditions and disease progression. A typical question that is posed in such studies is finding genes with significant changes between sample groups. In this respect expression variability is regarded as a nuisance factor that is primarily of technical origin and complicates the data analysis. However, it is becoming apparent that the biological variation in gene expression might be an important molecular phenotype that can affect physiological parameters. In this review we explore the recent literature on technical and biological variability in gene expression, sources of expression variability, (epi-)genetic hallmarks, and evolutionary constraints in genes with robust and variable gene expression. We provide an overview of recent findings on effects of external cues, such as diet and aging, on expression variability and on other biological phenomena that can be linked to it. We discuss metrics and tools that were developed for quantification of expression variability and highlight the importance of future studies in this direction. To assist the adoption of expression variability analysis, we also provide a detailed description and computer code, which can easily be utilized by other researchers. We also provide a reanalysis of recently published data to highlight the value of the analysis method.

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“…A confounding factor here is the existence of extensive alternative transcription within protein-coding genes. The proportion of this complexity that represents stochastic "noise" remains to be ascertained (Pertea et al 2018;Wan and Larson 2018), and although it could be that only a minority of transcript isoforms are translated into mature proteins, this remains highly debated (Mudge et al 2013;Blencowe 2017;Tress et al 2017). In fact, we believe that PhyloCSF and our PCCR list have enormous potential both to discover additional novel protein-coding alternatively spliced transcripts in known genes and to distinguish those known transcripts that generate conserved protein products from those that do not (see Supplemental Fig.…”

Section: Discussionmentioning

confidence: 99%