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Long non-coding RNAs (LNC) regulate numerous biological processes. In contrast to human, the identification of LNC in farm species, like chicken, is still lacunar. We propose a catalogue of 52,075 chicken genes enriched in LNC ( http://www.fragencode.org/ ), built from the Ensembl reference extended using novel LNC modelled here from 364 RNA-seq and LNC from four public databases. The Ensembl reference grew from 4,643 to 30,084 LNC, of which 59% and 41% with expression ≥ 0.5 and ≥ 1 TPM respectively. Characterization of these LNC relatively to the closest protein coding genes (PCG) revealed that 79% of LNC are in intergenic regions, as in other species. Expression analysis across 25 tissues revealed an enrichment of co-expressed LNC:PCG pairs, suggesting co-regulation and/or co-function. As expected LNC were more tissue-specific than PCG (25% vs. 10%). Similarly to human, 16% of chicken LNC hosted one or more miRNA. We highlighted a new chicken LNC, hosting miR155, conserved in human, highly expressed in immune tissues like miR155, and correlated with immunity-related PCG in both species. Among LNC:PCG pairs tissue-specific in the same tissue, we revealed an enrichment of divergent pairs with the PCG coding transcription factors, as for example LHX5, HXD3 and TBX4, in both human and chicken.

The development of High Throughput Sequencing (HTS) for RNA profiling (RNA-seq) has shed light on the diversity of transcriptomes. While RNA-seq is becoming a de facto standard for monitoring the population of expressed transcripts in a given condition at a specific time, processing the huge amount of data it generates requires dedicated bioinformatics programs. Here, we describe a standard bioinformatics protocol using state-of-the-art tools, the STAR mapper to align reads onto a reference genome, Cufflinks to reconstruct the transcriptome, and RSEM to quantify expression levels of genes and transcripts. We present the workflow using human transcriptome sequencing data from two biological replicates of the K562 cell line produced as part of the ENCODE3 project.

Alternative splicing (AS) is a eukaryotic principle to derive more than one RNA product from transcribed genes by removing distinct subsets of introns from a premature polymer. We know today that this process is highly regulated and makes up a large part of the differences between species, cell types, and states. The key to compare AS across different genes or organisms is to tokenize the AS phenomenon into atomary units, so-called AS events. These events then usually are grouped by common patterns to investigate the underlying molecular mechanisms that drive their regulation. However, attempts to decompose loci with AS observations into events are often hampered by applying a limited set of a priori defined event patterns which are not capable to describe all AS configurations and therefore cannot decompose the phenomenon exhaustively. In this chapter, we describe working scenarios of AStalavista, a computational method that reports all AS events reflected by transcript annotations. We show how to practically employ AStalavista to study AS variation in complex transcriptomes, as characterized by the human GENCODE annotation. Our examples demonstrate how the inherent and universal AStalavista paradigm allows for an automatic delineation of AS events in custom gene datasets. Additionally, we sketch an example of an AStalavista use case including next-generation sequencing data (RNA-Seq) to enrich the landscape of discovered AS events.

In the process of establishing more and more complete annotations of eukaryotic genomes, a constantly growing number of alternative splicing (AS) events has been reported over the last decade. Consequently, the increasing transcript coverage also revealed the real complexity of some variations in the exon-intron structure between transcript variants and the need for computational tools to address 'complex' AS events. ASTALAVISTA (alternative splicing transcriptional landscape visualization tool) employs an intuitive and complete notation system to univocally identify such events. The method extracts AS events dynamically from custom gene annotations, classifies them into groups of common types and visualizes a comprehensive picture of the resulting AS landscape. Thus, ASTALAVISTA can characterize AS for whole transcriptome data from reference annotations (GENCODE, REFSEQ, ENSEMBL) as well as for genes selected by the user according to common functional/structural attributes of interest: http://genome.imim.es/astalavista.