Revised Transcriptome-Based Gene Annotation for Aspergillus flavus Strain NRRL 3357

ABSTRACT Aspergillus flavus is an agriculturally and medically important filamentous fungus that produces mycotoxins, including aflatoxins, which are potent carcinogens. Here, we generated short- and long-read transcript sequence data from the growth of A. flavus strain NRRL 3357 under both typical and stress conditions to produce a new annotation of its genome.

Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features

Background Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. Some lncRNAs show altered expression levels in autistic brains, but their roles in ASD pathogenesis are still unclear. Results In this study, we have developed a new machine learning approach to predict candidate lncRNAs associated with ASD. Particularly, the knowledge learnt from protein-coding ASD risk genes was transferred to the prediction and prioritization of ASD-associated lncRNAs. Both developmental brain gene expression data and transcript sequence were found to contain relevant information for ASD risk gene prediction. During the pre-training phase of model construction, an autoencoder network was implemented for a representation learning of the gene expression data, and a random-forest-based feature selection was applied to the transcript-sequence-derived k-mers. Our models, including logistic regression, support vector machine and random forest, showed robust performance based on tenfold cross-validations as well as candidate prioritization with hypothetical loci. We then utilized the models to predict and prioritize a list of candidate lncRNAs, including some reported to be cis-regulators of known ASD risk genes, for further investigation. Conclusions Our results suggest that ASD risk genes can be accurately predicted using developmental brain gene expression data and transcript sequence features, and the models may provide useful information for functional characterization of the candidate lncRNAs associated with ASD.

The Meaning of the Microprocessor: Accounting for Evolution of Structural-Functional Novelty in the Canonical microRNA Biogenesis Pathway

Subsidiary to detection and assignment of novel microRNAs in non-model taxa, it is standard to identify and compare genomic or transcript sequence of Drosha and Pasha. Detection of both (1) bona fide microRNAs and (2) presence of Drosha/Pasha orthologs is often assumed to represent a functional canonical eumetazoan microRNA biogenesis pathway. However, this is not often experimentally confirmed in non-model taxa, and therefore the assumption is not necessarily valid. Below I describe several lines of evidence for this assertion.

Generation and characterization of the blood transcriptome of Macaca thibetana and comparative analysis with M. mulatta

Transcriptome profiles provide a large transcript sequence data set for genomic study, particularly in organisms that have no accurate genome data published.