Abstract

The transcriptome provides a functional footprint of the genome by enumerating the molecular components of cells and tissues. The field of transcript discovery has been revolutionized through high-throughput mRNA sequencing (RNA-seq). Here, we present a methodology that replicates and improves existing methodologies, and implements a workflow for error estimation and correction followed by genome annotation and transcript abundance estimation for RNA-seq derived transcriptome sequences (YeATS - Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). A unique feature of YeATS is the upfront determination of the errors in the sequencing or transcript assembly process by analyzing open reading frames of transcripts. YeATS identifies transcripts that have not been merged, result in broken open reading frames or contain long repeats as erroneous transcripts. We present the YeATS workflow using a representative sample of the transcriptome from the tissue at the heartwood/sapwood transition zone in black walnut. A novel feature of the transcriptome that emerged from our analysis was the identification of a highly abundant transcript that had no known homologous genes (GenBank accession: KT023102). The amino acid composition of the longest open reading frame of this gene classifies this as a putative extensin. Also, we corroborated the transcriptional abundance of proline-rich proteins, dehydrins, senescence-associated proteins, and the DNAJ family of chaperone proteins. Thus, YeATS presents a workflow for analyzing RNA-seq data with several innovative features that differentiate it from existing software.

Highlights

  • Analysis of the complete set of RNA molecules in a cell, the transcriptome, is critical to understanding the functional aspects of the genome of an organism

  • The input dataset to the YeATS tool was a set of transcripts, transcript identifiers and their corresponding raw counts, obtained from the tissue at the heartwood/sapwood transition zone (TZ) in black walnut (Juglans nigra L.) (Figure 2)

  • README FASTADIR.tgz : 24k transcripts ORFS.tgz : open reading frames from 24k transcripts computed from the ‘getorf’ tool from the Emboss suite. list.merged.txt : transcripts that have been merged based on overlapping ends High.TZ.genome.annotated.csv : transcripts having only one ORF with a high significance match Lower.TZ.genome.annotated.csv : transcripts having only one ORF with a lower significance match TZ.genome.annotated.none.csv : transcripts with no match TZ.genome.errors : transcripts which have two ORFs matching with high significance to the same gene TZ.genome.annotated.morethanone.csv : transcripts having more than one ORFs which match to different genes with high significance rawcounts.TZ: Raw counts rawcounts.normalized.TZ: Normalized counts

Read more

Summary

Introduction

Analysis of the complete set of RNA molecules in a cell, the transcriptome, is critical to understanding the functional aspects of the genome of an organism. Non-translated transcripts (noncoding RNAs) may be alternatively spliced and/or broken into smaller RNAs, the importance of which have only recently been recognized[2]. Transcriptional levels vary significantly based on environmental cues[3], and/or disease[4]. Quantifying transcriptional levels constitutes an important methodology in current biological research. Traditional methods like RNA:DNA hybridization[5] and short sequence-based approaches[6] have been supplanted recently by a high-throughput DNA sequencing method - RNA-seq[7,8]. Concomitant with the introduction of RNA-seq has been the development of a diverse set of computational methods for analyzing the resultant data[9,10,11,12,13,14,15,16,17,18,19,20,21]

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call