A Method for Identifying Splice Sites and Translation Start Sites in Human Genomic Sequences

Kim, Ki-Bong; Park, Kiejung; Kong, Eun Bae

doi:10.5483/bmbrep.2002.35.5.513

Cited by 3 publications

(12 citation statements)

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“…2) deSALT enables to handle relatively short exons. We assessed the alignment of the bases putatively from short exons by a series of #ExonGA(x) statistics ( Figures 4D-F), i.e., ExonGA (20), ExonGA(30), ExonGA (40), ExonGA(50) and ExonGA(60). The results demonstrate that deSALT enables the recovery of a higher number of short exons.…”

Section: Results On Real Sequencing Datasetsmentioning

confidence: 99%

“…A local sequence-based scoring system [40] is then employed to refine the draft exons (Section 3.2 of Supplementary Notes). For each of the draft exons, deSALT selects two small flanking regions.…”

Section: Exon Inferencementioning

confidence: 99%

“…2) Exon inference: deSALT maps all the alignment skeletons to the reference and infers potential exons from the projections of the skeletons. A local sequence-based scoring system [40] is employed to be a refined exon.…”

Section: Steps Of the Desalt Approachmentioning

confidence: 99%

“…deSALT treats known gene isoforms as a special kind of alignment skeletons, and it also maps them to the reference genome so that the genomic regions covered by known gene isoforms and the alignment skeletons are combined together. The combined regions are then recognized as draft exons, and their lengths and alignment skeleton coverages are calculated.The ones with too short a length and too low coverage are then filtered out.A local sequence-based scoring system [40] is then employed to refine the draft exons (Section 3.2 of Supplementary Notes). For each of the draft exons, deSALT selects two small flanking regions.The scoring system uses pre-defined acceptor and donor scoring matrixes to score each of the positions in the upstream and downstream regions respectively.…”

mentioning

confidence: 99%

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deSALT: fast and accurate long transcriptomic read alignment with de Bruijn graph-based index

Liu

Zang

et al. 2019

Preprint

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Long-read RNA sequencing (RNA-seq) is promising to transcriptomics studies, however, the alignment of long RNA-seq reads is still non-trivial due to high sequencing errors and complicated gene structures. Herein, we propose deSALT, a tailored two-pass alignment approach, which constructs graph-based alignment skeletons to infer exons and uses them to generate spliced reference sequences to produce refined alignments. deSALT addresses several difficult technical issues, such as small exons and sequencing errors, which breakthroughs the bottlenecks of long RNA-seq read alignment. Benchmarks demonstrate that deSALT has a greater ability to produce accurate and homogeneous full-length alignments. deSALT is available at: https://github.com/hitbc/deSALT. Keywords long read alignment, RNA-seq, de Bruijn graph-based index 3 Background RNA sequencing (RNA-seq) has become a fundamental approach to characterize transcriptomes.It reveals precise gene structures and quantifies gene/transcript expressions [1][2][3][4][5] in various applications, such as variant calling [6], RNA editing analysis [7, 8], and gene fusion detection [9, 10].However, current widely used short read sequencing technologies have limited read length and systematic bias from library preparation. These drawbacks limit more accurate alignment [11] and precise gene isoform analysis [12], thus creating a bottleneck for transcriptomic studies.Two kinds of long read sequencing technologies, i.e., single molecule real time (SMRT) sequencing produced by Pacific Biosciences (PacBio) [13] and nanopore sequencing produced by Oxford Nanopore Technologies (ONT) [14], are emerging and promising to breakthrough the bottleneck of short reads in transcriptomic analysis. Both of them enable the production of much longer reads, the mean and maximum lengths of the reads being over ten to hundreds of thousands of base pairs (bp) [15,16], respectively. Taking this advantage, full-length transcripts can be sequenced by single reads, which is promising for substantially improving the accuracy of gene isoform reconstruction. Furthermore, there is less systematic bias in the sequencing procedure [17], which is also beneficial to gene/transcript expression quantification.Besides their advantages, PacBio and ONT reads have much higher sequencing error rates than that of short reads. For PacBio SMRT sequencing, the sequencing error rate of raw reads ("subreads")is about 10% to 20% [16]; for ONT nanopore sequencing, the sequencing error rates of 1D and 2D (also known as 1D 2 ) reads are about 25% and 12% [18,19], respectively. PacBio SMRT platforms can produce reads of inserts (ROIs) by sequencing circular fragments multiple times to largely reduce sequencing errors. However, this technology has lower sequencing yields and reduced read lengths. Therefore, these high sequencing errors raise new technical challenges for RNA-seq data analysis.Read alignment could be the most affected one, and the effect may not be limited to the read alignment itself since it is fundamental to many down...

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Section: Results On Real Sequencing Datasetsmentioning

confidence: 99%

Section: Exon Inferencementioning

confidence: 99%

Section: Steps Of the Desalt Approachmentioning

confidence: 99%

mentioning

confidence: 99%

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deSALT: fast and accurate long transcriptomic read alignment with de Bruijn graph-based index

Liu

Zang

et al. 2019

Preprint

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“…The ratio of footprints per nucleotide for the translation start codon +/− 1 nucleotide and the footprints per nucleotide for the entire ORF provide a measure of footprint accumulation on the translation start codon. Cutoffs for ORFs and 5’ UTRs were as for B and C. e Boxplots showing the distribution of a Kozak consensus sequence score (methods section and [ 25 ] for all AUG uORFs of transcripts expressed in murine ES cells (>8.5 normalized transcriptome reads, n = 16246), uORFs without footprints ( n = 5486), with at least 20 footprints ( n = 2911), DMDA-PatA ( n = 1195). f A well translated uORF in Collagen 3a1.…”

Section: Resultsmentioning

confidence: 99%

Pateamine A-sensitive ribosome profiling reveals the scope of translation in mouse embryonic stem cells

et al. 2016

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BackgroundOpen reading frames are common in long noncoding RNAs (lncRNAs) and 5’UTRs of protein coding transcripts (uORFs). The question of whether those ORFs are translated was recently addressed by several groups using ribosome profiling. Most of those studies concluded that certain lncRNAs and uORFs are translated, essentially based on computational analysis of ribosome footprints. However, major discrepancies remain on the scope of translation and the translational status of individual ORFs. In consequence, further criteria are required to reliably identify translated ORFs from ribosome profiling data.ResultsWe examined the effect of the translation inhibitors pateamine A, harringtonine and puromycin on murine ES cell ribosome footprints. We found that pateamine A, a drug that targets eIF4A, allows a far more accurate identification of translated sequences than previously used drugs and computational scoring schemes. Our data show that at least one third but less than two thirds of ES cell lncRNAs are translated. We also identified translated uORFs in hundreds of annotated coding transcripts including key pluripotency transcripts, such as dicer, lin28, trim71, and ctcf.ConclusionPateamine A inhibition data clearly increase the precision of the detection of translated ORFs in ribosome profiling experiments. Our data show that translation of lncRNAs and uORFs in murine ES cells is rather common although less pervasive than previously suggested. The observation of translated uORFs in several key pluripotency transcripts suggests that translational regulation by uORFs might be part of the network that defines mammalian stem cell identity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2384-0) contains supplementary material, which is available to authorized users.

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deSALT: fast and accurate long transcriptomic read alignment with de Bruijn graph-based index

Liu

et al. 2019

Genome Biol

View full text Add to dashboard Cite

The alignment of long-read RNA sequencing reads is non-trivial due to high sequencing errors and complicated gene structures. We propose deSALT, a tailored two-pass alignment approach, which constructs graph-based alignment skeletons to infer exons and uses them to generate spliced reference sequences to produce refined alignments. deSALT addresses several difficult technical issues, such as small exons and sequencing errors, which break through bottlenecks of long RNA-seq read alignment. Benchmarks demonstrate that deSALT has a greater ability to produce accurate and homogeneous full-length alignments. deSALT is available at: https://github.com/hitbc/deSALT.

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A Method for Identifying Splice Sites and Translation Start Sites in Human Genomic Sequences

Cited by 3 publications

References 12 publications

deSALT: fast and accurate long transcriptomic read alignment with de Bruijn graph-based index

deSALT: fast and accurate long transcriptomic read alignment with de Bruijn graph-based index

Pateamine A-sensitive ribosome profiling reveals the scope of translation in mouse embryonic stem cells

deSALT: fast and accurate long transcriptomic read alignment with de Bruijn graph-based index

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