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Emilio Palumbo

Publications and source records attributed to Emilio Palumbo.

3 recordsLinked to original sources

ChimPipe: Accurate detection of fusion genes and transcription-induced chimeras from RNA-seq data

BackgroundChimeric transcripts are commonly defined as transcripts linking two or more different genes in the genome, and can be explained by various biological mechanisms such as genomic rearrangement, read-through or trans-splicing, but also by technical or biological artefacts. Several studies have shown their importance in cancer, cell pluripotency and motility. Many programs have recently been developed to identify chimeras from Illumina RNA-seq data (mostly fusion genes in cancer). However outputs of different programs on the same dataset can be widely inconsistent, and tend to include many false positives. Other issues relate to simulated datasets restricted to fusion genes, real datasets with limited numbers of validated cases, result inconsistencies between simulated and real datasets, and gene rather than junction level assessment.\n\nResultsHere we present ChimPipe, a modular and easy-to-use method to reliably identify chimeras from paired-end Illumina RNA-seq data. We have also produced realistic simulated datasets for three different read lengths, and enhanced two gold-standard cancer datasets by associating exact junction points to validated gene fusions. Benchmarking ChimPipe together with four other state-of-the-art tools on this data showed ChimPipe to be the top program at identifying exact junction coordinates for both kinds of datasets, and the one showing the best trade-off between sensitivity and precision. Applied to 106 ENCODE human RNA-seq datasets, ChimPipe identified 137 high confidence chimeras connecting the protein coding sequence of their parent genes. In subsequent experiments, three out of four predicted chimeras, two of which recurrently expressed in a large majority of the samples, could be validated. Cloning and sequencing of the three cases revealed several new chimeric transcript structures, 3 of which with the potential to encode a chimeric protein for which we hypothesized a new role.\n\nConclusionsChimPipe combines spanning and paired end RNA-seq reads to detect any kind of chimeras, including read-throughs, and shows an excellent trade-off between sensitivity and precision. The chimeras found by ChimPipe can be validated in-vitro with high accuracy.

Bioinformatics

Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features.

Long noncoding RNAs (lncRNAs) represent a vast unexplored genetic space that may hold missing drivers of tumourigenesis, but few such \"driver lncRNAs\" are known. Until now, they have been discovered through changes in expression, leading to problems in distinguishing between causative roles and passenger effects. We here present a different approach for driver lncRNA discovery using mutational patterns in tumour DNA. Our pipeline, ExInAtor, identifies genes with excess load of somatic single nucleotide variants (SNVs) across panels of tumour genomes. Heterogeneity in mutational signatures between cancer types and individuals is accounted for using a simple local trinucleotide background model, which yields high precision and low computational demands. We use ExInAtor to predict drivers from the GENCODE annotation across 1112 entire genomes from 23 cancer types. Using a stratified approach, we identify 15 high-confidence candidates: 9 novel and 6 known cancer-related genes, including MALAT1, NEAT1 and SAMMSON. Both known and novel driver lncRNAs are distinguished by elevated gene length, evolutionary conservation and expression. We have presented a first catalogue of mutated lncRNA genes driving cancer, which will grow and improve with the application of ExInAtor to future tumour genome projects.

Cancer Biology

Scalable Design of Paired CRISPR Guide RNAs for Genomic Deletion

Using CRISPR/Cas9, diverse genomic elements may be studied in their endogenous context. Pairs of single guide RNAs (sgRNAs) are used to delete regulatory elements and small RNA genes, while longer RNAs can be silenced through promoter deletion. We here present CRISPETa, a bioinformatic pipeline for flexible and scalable paired sgRNA design based on an empirical scoring model. Multiple sgRNA pairs are returned for each target. Any number of targets can be analyzed in parallel, making CRISPETa equally appropriate for studies of individual elements, or complex library screens. Fast run-times are achieved using a precomputed off-target database. sgRNA pair designs are output in a convenient format for visualisation and oligonucleotide ordering. We present a series of pre-designed, high-coverage library designs for entire classes of non-coding elements in human, mouse, zebrafish, Drosophila and C. elegans. Using an improved version of the DECKO deletion vector, together with a quantitative deletion assay, we test CRISPETa designs by deleting an enhancer and exonic fragment of the MALAT1 oncogene. These achieve efficiencies of [≥]50%, resulting in production of mutant RNA. CRISPETa will be useful for researchers seeking to harness CRISPR for targeted genomic deletion, in a variety of model organisms, from single-target to high-throughput scales.

Genomics