Transcriptomics technologies: Difference between revisions

Revision as of 05:08, 3 November 2021

Revision as of 21:55, 7 December 2021

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==== Alignment ====

In order to link sequence read abundance to the expression of a particular gene, transcript sequences are [[Sequence alignment|aligned]] to a reference genome or [[De novo transcriptome assembly|''de novo'' aligned]] to one another if no reference is available.<ref name="#23222703" /><ref name="#24532719" /> <ref> {{Cite journal|last1=Siadjeu|first1=Christian|last2=Mayland-Quellhorst|first2=Eike|last3=Pande|first3=Shruti|last4=Laubinger|first4=Sascha|last5=Albach|first5=Dirk C.|date=2021|title=Transcriptome Sequence Reveals Candidate Genes Involving in the Post-Harvest Hardening of Trifoliate Yam Dioscorea dumetorum|journal=Plants|language=en|volume=10|issue=4|pages=787|doi=10.3390/plants10040787|pmid=33923758|pmc=8074181|doi-access=free}} </ref> The key challenges for [[List of sequence alignment software|alignment software]] include sufficient speed to permit billions of short sequences to be aligned in a meaningful timeframe, flexibility to recognise and deal with intron splicing of eukaryotic mRNA, and correct assignment of reads that map to multiple locations. Software advances have greatly addressed these issues, and increases in sequencing read length reduce the chance of ambiguous read alignments. A list of currently available high-throughput sequence aligners is maintained by the [[European Bioinformatics Institute|EBI]].<ref>HTS Mappers. http://www.ebi.ac.uk/~nf/hts_mappers/</ref><ref name="#23060614">{{cite journal | vauthors = Fonseca NA, Rung J, Brazma A, Marioni JC | title = Tools for mapping high-throughput sequencing data | journal = Bioinformatics | volume = 28 | issue = 24 | pages = 3169–77 | date = December 2012 | pmid = 23060614 | doi = 10.1093/bioinformatics/bts605 | doi-access = free }}</ref>

Alignment of [[Primary transcript|primary transcript mRNA]] sequences derived from [[eukaryote]]s to a reference genome requires specialised handling of [[intron]] sequences, which are absent from mature mRNA.<ref>{{cite journal | vauthors = Trapnell C, Pachter L, Salzberg SL | title = TopHat: discovering splice junctions with RNA-Seq | journal = Bioinformatics | volume = 25 | issue = 9 | pages = 1105–11 | date = May 2009 | pmid = 19289445 | doi = 10.1093/bioinformatics/btp120 | pmc = 2672628 }}</ref> Short read aligners perform an additional round of alignments specifically designed to identify [[splice junction]]s, informed by canonical splice site sequences and known intron splice site information. Identification of intron splice junctions prevents reads from being misaligned across splice junctions or erroneously discarded, allowing more reads to be aligned to the reference genome and improving the accuracy of gene expression estimates. Since [[Regulation of gene expression|gene regulation]] may occur at the [[Alternative splicing|mRNA isoform]] level, splice-aware alignments also permit detection of isoform abundance changes that would otherwise be lost in a bulked analysis.<ref name="#20436464">{{cite journal | vauthors = Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L | title = Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation | journal = Nature Biotechnology | volume = 28 | issue = 5 | pages = 511–5 | date = May 2010 | pmid = 20436464 | pmc = 3146043 | doi = 10.1038/nbt.1621 }}</ref>

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<small>Legend: RAM – random access memory; MPI – message passing interface; EST – expressed sequence tag.</small>

==== Quantification ====

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==== A transcriptome based aging clock ====

Aging-related preventive interventions are not possible without personal aging speed measurement. The most up to date and complex way to measure aging rate is by using varying biomarkers of human aging is based on the utilization of deep neural networks which may be trained on any type of omics biological data to predict the subject’ssubject's age. Aging has been shown to be a strong driver of transcriptome changes.<ref name="clock">{{cite journal | vauthors = Meyer DH, Schumacher B | year = 2020 | title = BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy | journal = Aging Cell| volume = 20 | issue = 3 | pages = e13320 | doi = 10.1111/acel.13320 | pmid = 33656257 | pmc = 7963339 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Fleischer JG, Schulte R, Tsai HH, Tyagi S, Ibarra A, Shokhirev MN, Navlakha S | year = 2018 | title = Predicting age from the transcriptome of human dermal fibroblasts | journal = Genome Biology | volume = 19 | issue = 1| page = 221 | doi = 10.1186/s13059-018-1599-6 | pmid = 30567591 | pmc = 6300908 }}</ref> Aging clocks based on transcriptomes have suffered from considerable variation in the data and relatively low accuracy. However an approach that uses temporal scaling and binarization of transcriptomes to define a gene set that predicts biological age with an accuracy allowed to reach an assessment close to the theoretical limit.<ref name="clock" />

=== Non-coding RNA ===