Biology subjects

Valen, E.

Publications and source records attributed to Valen, E..

5 recordsLinked to original sources

bioRxiv2018

Trans-splicing of mRNAs links gene transcription to translational control regulated by mTOR

In phylogenetically diverse organisms, the 5 ends of a subset of mRNAs are trans-spliced with a spliced leader (SL) RNA. The functions of SL trans-splicing, however, remain largely enigmatic. Here, we quantified translation genome-wide in the marine chordate, Oikopleura dioica, under inhibition of mTOR, a central growth regulator. Translation of trans-spliced TOP mRNAs was suppressed, showing that the SL sequence permits nutrient-dependent translational control of growth-related mRNAs. Under crowded, nutrient-limiting conditions, O. dioica continues to filter-feed, but arrests growth until favorable conditions return. Upon release from such conditions, initial recovery was independent of nutrient-responsive, trans-spliced genes, suggesting animal density sensing as a first trigger for resumption of development. Our results demonstrate a role for trans-splicing in the coordinated translational down-regulation of nutrient-responsive genes under limiting conditions.

genomics↗

bioRxiv2018

Shoelaces: an interactive tool for ribosome profiling processing and visualization

The emergence of ribosome profiling to map actively translating ribosomes has laid the foundation for a diverse range of studies on translational regulation. The data obtained with different variations of this assay is typically manually processed, which has created a need for tools that would streamline and standardize processing steps.\n\nWe present Shoelaces, a toolkit for ribosome profiling experiments automating read selection and filtering to obtain genuine translating footprints. Based on periodicity, favoring enrichment over the coding regions, it determines the read lengths corresponding to bona fide ribosome protected fragments. The specific codon under translation (P-site) is determined by automatic offset calculations resulting in sub-codon resolution. Shoelaces provides both a user-friendly graphical interface for interactive visualisation in a genome browser-like fashion and a command line interface for integration into automated pipelines. We process 79 libraries and show that studies typically discard excessive amounts of data in their manual analysis pipelines.\n\nShoelaces streamlines ribosome profiling analysis offering automation of the processing, a range of interactive visualization features and export of the data into standard formats. Shoelaces stores all processing steps performed in an XML file that can be used by other groups to exactly reproduce the processing of a given study. We therefore anticipate that Shoelaces can aid researchers by automating what is typically performed manually and contribute to the overall reproducibility of studies. The tool is freely distributed as a Python package, with additional instructions and demo datasets available at https://bitbucket.org/valenlab/shoelaces

bioinformatics↗

bioRxiv2018

Accurate analysis of genuine CRISPR editing events with ampliCan

We present ampliCan, an analysis tool for genome editing that unites highly precise quantification and visualization of genuine genome editing events. ampliCan features nuclease-optimized alignments, filtering of experimental artifacts, event-specific normalization, off-target read detection and quantifies insertions, deletions, HDR repair as well as targeted base editing. It is scalable to thousands of amplicon sequencing-based experiments from any genome editing experiment, including CRISPR. It enables automated integration of controls and accounts for biases at every step of the analysis. We benchmarked ampliCan on both real and simulated datasets against other leading tools, demonstrating that it outperformed all in the face of common confounding factors.

bioinformatics↗

bioRxiv2017

REPARATION: Ribosome Profiling Assisted (Re-)Annotation of Bacterial genomes.

Prokaryotic genome annotation is highly dependent on automated methods, as manual curation cannot keep up with the exponential growth of sequenced genomes. Current automated methods depend heavily on sequence context and often underestimate the complexity of the proteome. We developed REPARATION (RibosomeE Profiling Assisted (Re-)AnnotaTION), a de novo algorithm that takes advantage of experimental protein translation evidence from ribosome profiling (Ribo-seq) to delineate translated open reading frames (ORFs) in bacteria, independent of genome annotation. REPARATION evaluates all possible ORFs in the genome and estimates minimum thresholds based on a growth curve model to screen for spurious ORFs. We applied REPARATION to three annotated bacterial species to obtain a more comprehensive mapping of their translation landscape in support of experimental data. In all cases, we identified hundreds of novel (small) ORFs including variants of previously annotated ORFs. Our predictions were supported by matching mass spectrometry (MS) proteomics data, sequence composition and conservation analysis. REPARATION is unique in that it makes use of experimental translation evidence to perform de novo ORF delineation in bacterial genomes irrespective of the sequence context of the reading frame.

microbiology↗

bioRxiv2016

A Unique Ribosome Signature Reveals Bacterial Translation Initiation Sites

ABSTRCTWhile methods for annotation of genes are increasingly reliable the exact identification of the translation initiation site remains a challenging problem. Since the N-termini of proteins often contain regulatory and targeting information developing a robust method for start site identification is crucial. Ribosome profiling reads show distinct patterns of read length distributions around translation initiation sites. These patterns are typically lost in standard ribosome profiling analysis pipelines, when reads from footprints are adjusted to determine the specific codon being translated. Using these unique signatures we build a model capable of predicting translation initiation sites and demonstrate its high accuracy using N-terminal proteomics. Applying this to prokaryotic samples, we re-annotate translation initiation sites and provide evidence of N-terminal truncations and elongations of annotated coding sequences. These re-annotations are supported by the presence of Shine-Dalgarno sequences, structural and sequence based features and N-terminal peptides. Finally, our model identifies 61 novel genes previously undiscovered in the genome.

bioinformatics↗