Introducing bollito: a flexible pipeline for comprehensive single-cell RNA-seq analyses

We are proud to announce bollito, an automated, flexible and parallelizable computational pipeline for the comprehensive analysis of single-cell RNA-seq data.

Starting from FASTQ files or pre-processed expression matrices, bollito performs both basic and advanced tasks in single-cell analysis including quality control, read alignment, dimensionality reduction, clustering, cell-marker detection, differential expression, functional analysis, trajectory inference and RNA velocity. We have built bollito using the Snakemake workflow management system, which easily connects each execution step and facilitates the reproducibility of results.

Are you interested in bollito? We have published the tool in Bioinformatics, check it out here.  The pipeline, along with full documentation and tutorials is available here.  

 

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Predicting isoforms functional importance with TRIFID

We are pleased to announce TRIFID, a Machine Learning-based method for predicting isoform functional importance.

The advent of long-read sequencing means that the number of annotated transcripts in reference databases will increase. Therefore, it is crucial to understand which protein isoforms are biologically relevant and which are not. TRIFID overcomes this challenge, harnessing proteomics evidence as a proxy for functionality.  To do so, the algorithm evaluates over 40 predictive features for both principal and alternative isoforms divided into 5 categories: annotation, evolution, expression, structure, and splicing. The model has been trained on isoforms detected in large-scale proteomics analyses to distinguish these biologically important splice isoforms with high confidence.

In particular, we hope TRIFID will be a particularly valuable tool to help understand the pathogenic effects of mutations on splice variants.

TRIFID has been published at NAR Genomics and Bioinformatics and is available here.

 

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Comienza la preinscripción del Máster de Bioinformática aplicada a la Medicina Personalizada y Salud

Desde ahora y hasta septiembre de 2021, abre el periodo de preinscripción para la 5ª Edición del Máster de Bioinformática aplicada a la Medicina Personalizada y la Salud. El máster está organizado por el Instituto de Salud Carlos III (ISCIII),  el Centro Nacional de Investigaciones Oncológicas (CNIO), el Barcelona Supercomputing Center (BSC) y la Sociedad Española de Biotecnología (SEBiot).  Un año más, será codirigido por la doctora Fátima Al-Shahrour, jefa de la Unidad de Bioinformática del CNIO y el doctor Alfonso Valencia, director del departamento de Ciencias de la Vida en el BSC.

El Máster en Bioinformática Aplicada a la Medicina Personalizada y la Salud es un Título Propio de la Escuela Nacional de Sanidad del Instituto de Salud Carlos III, dirigido a licenciados y profesionales de diversa formación, con interés en Biología, Biomedicina e Ingeniería Informática aplicadas al ámbito clínico y sanitario. Con una duración de 12 meses, el máster combina contenidos teóricos y prácticos, impartidos por un cuerpo docente con probada trayectoria investigadora y más de 15 años de experiencia en la formación de bioinformáticos en España.

Puedes preinscribirte en aquí.

 

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Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq

We are excited to announce Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments.

 

 

Beyondcell calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, our method determines therapeutic differences among cell populations, and generates a prioritised ranking of the differential sensitivity drugs between chosen conditions to guide drug selection.

Beyondcell’s potential applications include:

  • Analysing the intratumoural heterogeneity (ITH) of your experiment.
  • Grouping cells into clusters of concordant drug response.
  • Prioritizing cancer treatments.
  • Studying changes in drug tolerance on a time series experiment.
  • Identifying mechanisms of drug resistance.

A preprint has been published at bioRxiv and is available here. Beyondcell source code along with step by step tutorials are fully available here. Check Beyondcell’s thread on Twitter.

 

 

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Arranca la edición 2020-2021 del Máster de Bioinformática aplicada a la Medicina Personalizada y Salud.

A pesar de la pandemia de COVID-19, el pasado lunes arrancó un año más este título propio de la Escuela Nacional de Sanidad (ENS) del Instituto Carlos III de Madrid (ISCIII), codirigido por la doctora Fátima Al-Shahrour, jefa de la Unidad de Bioinformática del CNIO y el doctor Alfonso Valencia, director del departamento de Ciencias de la Vida en el Barcelona Supercomputing Center (BSC). A cargo de la coordinación academica se encuentran los doctores Gonzalo Gómez López y Tomás Di Domenico, ambos miembros de la Unidad de Bioinformática del CNIO.

Con una duración de 12 meses, el máster combina contenidos teóricos y prácticos, impartidos por un cuerpo docente con probada trayectoria investigadora y más de 15 años de experiencia en la formación de bioinformáticos en España. Además, pone a disposición de los alumnos la posibilidad de conocer a profesionales del sector a través de la serie de seminarios Conoce al experto y TFMs tanto nacionales como internacionales, adaptados a las necesidades de intereses de los estudiantes. Puedes consultar más información en la web oficial.

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DREIMT: exploring the druggable immune system

We are pleased to announce the publication of DREIMT: a bioinformatics tool for hypothesis generation and prioritization of drugs capable of modulating immune cells activity. At this moment, DREIMT integrates 4690 drug profiles from The Library of Network-Based Cellular Signatures (LINCS) L1000 dataset and 2,700 manually curated immune gene expression signatures to generate a compendium of drug-immune signature associations. From BU we would like to thanks the SING group and all our collaborators for making it possible!

 

The full article has been published at Bioinformatics and is available here.

 

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Harnessing miR-203 to enhance pluripotent stem cells differentiation

Pluripotent stem cells (PSCs) are a promising tool for regenerative medicine, given their self-renewal potential and ability to differentiate into multiple cell lineages. However, the differentiation capacity of PSCs frequently decreases during  in vitro expansion, hindering research efforts. In this regard, BU has contributed to a recent work showcasing miR-203, a single microRNA which improves […]

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Launched the Pan-Cancer Analysis of Whole Genomes. BU has contributed to this great achievement.

The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium has released the set of publications derived from the analysis of the Whole Genome Sequencing of more than 2,600 cancers of different tumor types. The findings and tools originated aim to further understand cancer complexity and uncover new therapeutic options.

We are very happy to have contributed to this work.

You can read the co-authored article here and the full set of resources on the collection’s page.

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PanDrugs article has been published in Genome Medicine!

We are proud to announce that PanDrugs paper has been published in Genome Medicine today. The article can be accessed here.

We want to thank all BU staff, SING group and all our collaborators for making it possible!

 

 

 

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