Tintle, N. | Search bioRxiv

bioRxiv2018

Transcriptome assembly and annotation of johnsongrass (Sorghum halepense) rhizomes identifies candidate rhizome specific genes

Rhizomes facilitate the wintering and vegetative propagation of many perennial grasses. Sorghum halepense (johnsongrass) is an aggressive perennial grass that relies on a robust rhizome system to persist through winters and reproduce asexually from its rootstock nodes. This study aimed to sequence and assemble expressed transcripts within the johnsongrass rhizome. A de novo transcriptome assembly was generated from a single johnsongrass rhizome meristem tissue sample. A total of 141,176 probable protein-coding sequences from the assembly were identified and assigned gene ontology terms using Blast2GO. The johnsongrass assembly was compared to Sorghum bicolor, a related non-rhizomatous species, along with an assembly of similar rhizome tissue from the perennial grain crop Thinopyrum intermedium. The presence/absence analysis yielded a set of 259 johnsongrass contigs that are likely associated with rhizome development.

genomics↗

bioRxiv2017

IMPROVEMENTS TO BAYESIAN GENE ACTIVITY STATE ESTIMATION FROM GENOME-WIDE TRANSCRIPTOMICS DATA

An important question in many biological applications, is to estimate or classify gene activity states (active or inactive) based on genome-wide transcriptomics data. Recently, we proposed a Bayesian method, titled MultiMM, which showed superior results compared to existing methods. In short, MultiMM performed better than existing methods on both simulated and real gene expression data, confirming well-known biological results and yielding better agreement with fluxomics data. Despite these promising results, MultiMM has numerous limitations. First, MultiMM leverages co-regulatory models to improve activity state estimates, but information about co-regulation is incorporated in a manner that assumes that networks are known with certainty. Second, MultiMM assumes that genes that change states in the dataset can be distinguished with certainty from those that remain in one state. Third, the model can be sensitive to extreme measures (outliers) of gene expression. In this manuscript, we propose a modified Bayesian approach, which addresses these three limitations by improving outlier handling and by explicitly modeling network and other uncertainty yielding improved gene activity state estimates when compared to MultiMM.

genomics↗