Transcriptome assembly strategies for precision medicine

BackgroundPrecision medicine approach holds great promise to tailored diagnosis, treatment and prevention. Individuals can be vastly different in their genomic information and genetic mechanisms hence having unique transcriptomic signatures. The development of precision medicine has demanded moving beyond DNA sequencing (DNA-Seq) to much more pointed RNA-sequencing (RNA-Seq) [Cell, 2017, 168: 584‒599].ResultsHere we conduct a brief survey on the recent methodology development of transcriptome assembly approach using RNA-Seq.ConclusionsSince transcriptomes in human disease are highly complex, dynamic and diverse, transcriptome assembly is playing an increasingly important role in precision medicine research to dissect the molecular mechanisms of the human diseases.

[1]  Daniel J. Gaffney,et al.  A survey of best practices for RNA-seq data analysis , 2016, Genome Biology.

[2]  Yongsheng Bai,et al.  Evaluation of de novo transcriptome assemblies from RNA-Seq data , 2014, Genome Biology.

[3]  Lior Pachter,et al.  Sequence Analysis , 2020, Definitions.

[4]  J. Rinn,et al.  Ab initio reconstruction of transcriptomes of pluripotent and lineage committed cells reveals gene structures of thousands of lincRNAs , 2010, Nature biotechnology.

[5]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.

[6]  Cole Trapnell,et al.  TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions , 2013, Genome Biology.

[7]  B. Taylor,et al.  Implementing Genome-Driven Oncology , 2017, Cell.

[8]  Steven J. M. Jones,et al.  De novo assembly and analysis of RNA-seq data , 2010, Nature Methods.

[9]  de Ng Dick Bruijn A combinatorial problem , 1946 .

[10]  Akhilesh K. Tyagi,et al.  De Novo Assembly of Chickpea Transcriptome Using Short Reads for Gene Discovery and Marker Identification , 2011, DNA research : an international journal for rapid publication of reports on genes and genomes.

[11]  Chittibabu Guda,et al.  Classification of breast cancer patients using somatic mutation profiles and machine learning approaches , 2016, BMC Systems Biology.

[12]  Xun Xu,et al.  SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads , 2013, Bioinform..

[13]  Eugene W. Myers,et al.  Toward Simplifying and Accurately Formulating Fragment Assembly , 1995, J. Comput. Biol..

[14]  Rajat K De,et al.  Precision medicine with electronic medical records: from the patients and for the patients. , 2016, Annals of translational medicine.

[15]  Chengying Shi,et al.  Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds , 2011, BMC Genomics.

[16]  Ben Ewen-Campen,et al.  De novo assembly and characterization of a maternal and developmental transcriptome for the emerging model crustacean Parhyale hawaiensis , 2011, BMC Genomics.

[17]  P. Pevzner,et al.  An Eulerian path approach to DNA fragment assembly , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Jeffrey S. Buguliskis Could RNA-Seq Become the Workhorse of Precision Medicine? , 2015 .

[19]  Xiuzhen Huang,et al.  Bridger: a new framework for de novo transcriptome assembly using RNA-seq data , 2015, Genome Biology.

[20]  N. Friedman,et al.  Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data , 2011, Nature Biotechnology.

[21]  E. Birney,et al.  Velvet: algorithms for de novo short read assembly using de Bruijn graphs. , 2008, Genome research.

[22]  Steven J. M. Jones,et al.  Abyss: a Parallel Assembler for Short Read Sequence Data Material Supplemental Open Access , 2022 .

[23]  Rui Chen,et al.  Promise of personalized omics to precision medicine , 2013, Wiley interdisciplinary reviews. Systems biology and medicine.

[24]  Xuan Li,et al.  Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study , 2011, BMC Bioinformatics.

[25]  M. Blaxter,et al.  Comparing de novo assemblers for 454 transcriptome data , 2010, BMC Genomics.

[26]  M. Fumagalli,et al.  Assessing the Effect of Sequencing Depth and Sample Size in Population Genetics Inferences , 2013, PloS one.

[27]  Zhong Wang,et al.  Next-generation transcriptome assembly , 2011, Nature Reviews Genetics.

[28]  David R. Kelley,et al.  Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks , 2012, Nature Protocols.

[29]  Baohong Zhang,et al.  De Novo Transcriptome Assembly and Comparative Analysis Elucidate Complicated Mechanism Regulating Astragalus chrysochlorus Response to Selenium Stimuli , 2015, PloS one.

[30]  Dimitrios I. Fotiadis,et al.  Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.

[31]  Siu-Ming Yiu,et al.  SOAP2: an improved ultrafast tool for short read alignment , 2009, Bioinform..

[32]  Cole Trapnell,et al.  Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. , 2010, Nature biotechnology.

[33]  G. Sherlock,et al.  Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads , 2010, BMC Genomics.

[34]  M. Yandell,et al.  A beginner's guide to eukaryotic genome annotation , 2012, Nature Reviews Genetics.

[35]  Anders Krogh,et al.  Bayesian transcriptome assembly , 2014, Genome Biology.

[36]  J. Rinn,et al.  Ab initio reconstruction of transcriptomes of pluripotent and lineage committed cells reveals gene structures of thousands of lincRNAs , 2010, Nature Biotechnology.

[37]  Colin N. Dewey,et al.  De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis , 2013, Nature Protocols.

[38]  Guojun Li,et al.  TransComb: genome-guided transcriptome assembly via combing junctions in splicing graphs , 2016, Genome Biology.

[39]  Martin Vingron,et al.  Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels , 2012, Bioinform..

[40]  Ulrich Keilholz,et al.  The combinatorial complexity of cancer precision medicine , 2014, Oncoscience.

[41]  Fuhong He,et al.  Modeling Transcriptome Based on Transcript-Sampling Data , 2008, PloS one.

[42]  Cole Trapnell,et al.  Computational methods for transcriptome annotation and quantification using RNA-seq , 2011, Nature Methods.