Background: Through the Community Sequencing Program, a catfish EST sequencing project was carried out through a collaboration between the catfish research community and the Department of Energy's Joint Genome Institute. Prior to this project, only a limited EST resource from catfish was available for the purpose of SNP identification. Results: A total of 438,321 quality ESTs were generated from 8 channel catfish (Ictalurus punctatus) and 4 blue catfish (Ictalurus furcatus) libraries, bringing the number of catfish ESTs to nearly 500,000. Assembly of all catfish ESTs resulted in 45,306 contigs and 66,272 singletons. Over 35% of the unique sequences had significant similarities to known genes, allowing the identification of 14,776 unique genes in catfish. Over 300,000 putative SNPs have been identified, of which approximately 48,000 are high-quality SNPs identified from contigs with at least four sequences and the minor allele presence of at least two sequences in the contig. The EST resource should be valuable for identification of microsatellites, genome annotation, large-scale expression analysis, and comparative genome analysis. Conclusions: This project generated a large EST resource for catfish that captured the majority of the catfish transcriptome. The parallel analysis of ESTs from two closely related Ictalurid catfishes should also provide powerful means for the evaluation of ancient and recent gene duplications, and for the development of high-density microarrays in catfish. The inter- and intra-specific SNPs identified from all catfish EST dataset assembly will greatly benefit the catfish introgression breeding program and whole genome association studies.

Plant biomass conversion for green chemistry and bio-energy is a current challenge for a modern sustainable bioeconomy. The complex polyaromatic lignin polymers in raw biomass feedstocks (i.e., agriculture and forestry by-products) are major obstacles for biomass conversions. White-rot fungi are wood decayers able to degrade all polymers from lignocellulosic biomass including cellulose, hemicelluloses, and lignin. The white-rot fungus Polyporus brumalis efficiently breaks down lignin and is regarded as having a high potential for the initial treatment of plant biomass in its conversion to bio-energy. Here, we describe the extraordinary ability of P. brumalis for lignin degradation using its enzymatic arsenal to break down wheat straw, a lignocellulosic substrate that is considered as a biomass feedstock worldwide. We performed integrative multi-omics analyses by combining data from the fungal genome, transcriptomes, and secretomes. We found that the fungus possessed an unexpectedly large set of genes coding for Class II peroxidases involved in lignin degradation (19 genes) and GMC oxidoreductases/dehydrogenases involved in generating the hydrogen peroxide required for lignin peroxidase activity and promoting redox cycling of the fungal enzymes involved in oxidative cleavage of lignocellulose polymers (36 genes). The examination of interrelated multi-omics patterns revealed that eleven Class II Peroxidases were secreted by the fungus during fermentation and eight of them where tightly co-regulated with redox cycling enzymatic partners. As a peculiar feature of P. brumalis, we observed gene family extension, up-regulation and secretion of an abundant set of versatile peroxidases and manganese peroxidases, compared with other Polyporales species. The orchestrated secretion of an abundant set of these delignifying enzymes and redox cycling enzymatic partners could contribute to the delignification capabilities of the fungus. Our findings highlight the diversity of wood decay mechanisms present in Polyporales and the potentiality of further exploring this taxonomic order for enzymatic functions of biotechnological interest.

Switchgrass (Panicum virgatum L.) is an important bioenergy crop widely used for lignocellulosic research. While extensive transcriptomic analyses have been conducted on this species using short read-based sequencing techniques, very little has been reliably derived regarding alternatively spliced (AS) transcripts. We present an analysis of transcriptomes of six switchgrass tissue types pooled together, sequenced using Pacific Biosciences (PacBio) single-molecular long-read technology. Our analysis identified 105,419 unique transcripts covering 43,570 known genes and 8795 previously unknown genes. 45,168 are novel transcripts of known genes. A total of 60,096 AS transcripts are identified, 45,628 being novel. We have also predicted 1549 transcripts of genes involved in cell wall construction and remodeling, 639 being novel transcripts of known cell wall genes. Most of the predicted transcripts are validated against Illumina-based short reads. Specifically, 96% of the splice junction sites in all the unique transcripts are validated by at least five Illumina reads. Comparisons between genes derived from our identified transcripts and the current genome annotation revealed that among the gene set predicted by both analyses, 16,640 have different exon–intron structures. Overall, substantial amount of new information is derived from the PacBio RNA data regarding both the transcriptome and the genome of switchgrass.

RNA-sequencing (RNA-seq) enables in-depth exploration of transcriptomes, but typical sequencing depth often limits its comprehensiveness. In this study, we generated nearly 3 billion RNA-Seq reads, totaling 341[emsp14]Gb of sequence, from a Zea mays seedling sample. At this depth, a near complete snapshot of the transcriptome was observed consisting of over 90% of the annotated transcripts, including lowly expressed transcription factors. A novel hybrid strategy combining de novo and reference-based assemblies yielded a transcriptome consisting of 126,708 transcripts with 88% of expressed known genes assembled to full-length. We improved current annotations by adding 4,842 previously unannotated transcript variants and many new features, including 212 maize transcripts, 201 genes, 10 genes with undocumented potential roles in seedlings as well as maize lineage specific gene fusion events. We demonstrated the power of deep sequencing for large transcriptome studies by generating a high quality transcriptome, which provides a rich resource for the research community.