Spatial organisation of proto-oncogenes in human haematopoietic progenitor cells

The eukaryotic cell nucleus is a highly organised organelle, with distinct specialised sub- compartments responsible for specific nuclear functions. Within the context of this functional framework, the genome is organised, allowing contact between specific genomic regions and sub-compartments. Previous work has shown that genes in both cis and trans can make specific contacts with each other. I hypothesise that such a preferred juxtaposition may impact the propensity for specific cancerinitiating chromosomal translocations to occur. In this thesis, I describe how I have extended and developed a ligation based proximity assay known as enriched 4C. I have coupled this technique with high throughput sequencing to determine genomic regions that spatially co-associate with the proto-oncogenes MLL, ABL1 and BCR. In addition to further developing the laboratory protocol, I have created bioinformatics tools used in the analysis of the sequencing data. I find that the association profiles of the three genes show strong correlation to the binding profile of RNA polymerase II and other active marks, suggesting that transcribed genes have a propensity to associate with other transcribed regions of the genome. Each gene also exhibits a unique repertoire of preferred associations with specific regions of the genome. Significantly, I find that the most frequent trans association of BCR is telomeric chromosome 9, encompassing its recurrent translocation partner gene ABL1. Interestingly, ABL1 is not at the maximum point of interaction. I use DNA-fluorescence in-situ hybridisation to validate the e4C association. My data supports a hypothesis that gene transcription has a direct role on genome organisation. I suggest that preferred co-associations of genes at transcription factories may promote the occurrence of specific chromosomal translocations

HiCUP: pipeline for mapping and processing Hi-C data.

HiCUP is a pipeline for processing sequence data generated by Hi-C and Capture Hi-C (CHi-C) experiments, which are techniques used to investigate three-dimensional genomic organisation. The pipeline maps data to a specified reference genome and removes artefacts that would otherwise hinder subsequent analysis. HiCUP also produces an easy-to-interpret yet detailed quality control (QC) report that assists in refining experimental protocols for future studies. The software is freely available and has already been used for processing Hi-C and CHi-C data in several recently published peer-reviewed studies

Global reorganization of the nuclear landscape in senescent cells.

Cellular senescence has been implicated in tumor suppression, development, and aging and is accompanied by large-scale chromatin rearrangements, forming senescence-associated heterochromatic foci (SAHF). However, how the chromatin is reorganized during SAHF formation is poorly understood. Furthermore, heterochromatin formation in senescence appears to contrast with loss of heterochromatin in Hutchinson-Gilford progeria. We mapped architectural changes in genome organization in cellular senescence using Hi-C. Unexpectedly, we find a dramatic sequence- and lamin-dependent loss of local interactions in heterochromatin. This change in local connectivity resolves the paradox of opposing chromatin changes in senescence and progeria. In addition, we observe a senescence-specific spatial clustering of heterochromatic regions, suggesting a unique second step required for SAHF formation. Comparison of embryonic stem cells (ESCs), somatic cells, and senescent cells shows a unidirectional loss in local chromatin connectivity, suggesting that senescence is an endpoint of the continuous nuclear remodelling process during differentiation

VLSI et calcul formel

SIGLECNRS-CDST / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Cluster Flow: A user-friendly bioinformatics workflow tool [version 1; referees: 3 approved]

Pipeline tools are becoming increasingly important within the field of bioinformatics. Using a pipeline manager to manage and run workflows comprised of multiple tools reduces workload and makes analysis results more reproducible. Existing tools require significant work to install and get running, typically needing pipeline scripts to be written from scratch before running any analysis. We present Cluster Flow, a simple and flexible bioinformatics pipeline tool designed to be quick and easy to install. Cluster Flow comes with 40 modules for common NGS processing steps, ready to work out of the box. Pipelines are assembled using these modules with a simple syntax that can be easily modified as required. Core helper functions automate many common NGS procedures, making running pipelines simple. Cluster Flow is available with an GNU GPLv3 license on GitHub. Documentation, examples and an online demo are available at http://clusterflow.io

Cluster Flow: A user-friendly bioinformatics workflow tool [version 2; referees: 3 approved]

Pipeline tools are becoming increasingly important within the field of bioinformatics. Using a pipeline manager to manage and run workflows comprised of multiple tools reduces workload and makes analysis results more reproducible. Existing tools require significant work to install and get running, typically needing pipeline scripts to be written from scratch before running any analysis. We present Cluster Flow, a simple and flexible bioinformatics pipeline tool designed to be quick and easy to install. Cluster Flow comes with 40 modules for common NGS processing steps, ready to work out of the box. Pipelines are assembled using these modules with a simple syntax that can be easily modified as required. Core helper functions automate many common NGS procedures, making running pipelines simple. Cluster Flow is available with an GNU GPLv3 license on GitHub. Documentation, examples and an online demo are available at http://clusterflow.io

MultiQC: summarize analysis results for multiple tools and samples in a single report

<h2>Highlights</h2> <h3>Better configs</h3> <p>As of this release, you can now set all of your config variables via environment variables! (see <a href="https://multiqc.info/docs/getting_started/config/#config-with-environment-variables">docs</a>).</p> <p>Better still, YAML config files can now use string interpolation to parse environment variables within strings (see <a href="https://multiqc.info/docs/getting_started/config/#referencing-environment-variables-in-yaml-configs">docs</a>), eg:</p> <pre><code class="language-yaml">report_header_info: - Contact E-mail: !ENV "${NAME:info}@${DOMAIN:example.com}" </code></pre> <h3>Picard refactoring</h3> <p>In this release, there was a significant refactoring of the Picard module. It has been generalized for better code sharing with other Picard-based software, like Sentieon and Parabricks. As a result of this, the standalone Sentieon module was removed: Sentieon QC files will be interpreted directly as Picard QC files.</p> <p>If you were using the Sentieon module in your pipelines, make sure to update any places that reference the module name:</p> <ul> <li>MultiQC command line (e.g. replace <code>--module sentieon</code> with <code>--module picard</code>).</li> <li>MultiQC configs (e.g. replace <code>sentieon</code> with <code>picard</code> in options like <code>run_modules</code>, <code>exclude_modules</code>, <code>module_order</code>).</li> <li>Downstream code that relies on names of the files in <code>multiqc_data</code> or <code>multiqc_plots</code> saves (e.g., <code>multiqc_data/multiqc_sentieon_AlignmentSummaryMetrics.txt</code> becomes <code>multiqc_data/multiqc_picard_AlignmentSummaryMetrics.txt</code>).</li> <li>Code that parses data files like <code>multiqc_data/multiqc_data.json</code>.</li> <li>Custom plugins and templates that rely on HTML anchors (e.g. <code>#sentieon_aligned_reads</code> becomes <code>#picard_AlignmentSummaryMetrics</code>).</li> <li>Also, note that Picard fetches sample names from the commands it finds inside the QC headers (e.g. <code># net.sf.picard.analysis.CollectMultipleMetrics INPUT=Szabo_160930_SN583_0215_AC9H20ACXX.bam ...</code> -> <code>Szabo_160930_SN583_0215_AC9H20ACXX</code>), whereas the removed Sentieon module prioritized the QC file names. To revert to the old Sentieon approach, use the <a href="https://multiqc.info/docs/getting_started/config/#using-log-filenames-as-sample-names"><code>use_filename_as_sample_name</code> config flag</a>.</li> </ul> <h2>MultiQC updates</h2> <ul> <li>Config can be set with environment variables, including env var interpolation (<a href="https://github.com/ewels/MultiQC/pull/2178">#2178</a>)</li> <li>Try find config in <code>~/.config</code> or <code>$XDG_CONFIG_HOME</code> (<a href="https://github.com/ewels/MultiQC/pull/2183">#2183</a>)</li> <li>Better sample name cleaning with pairs of input filenames (<a href="https://github.com/ewels/MultiQC/pull/2181">#2181</a>)</li> <li>Software versions: allow any string as a version tag (<a href="https://github.com/ewels/MultiQC/pull/2166">#2166</a>)</li> <li>Table columns with non-numeric values and now trigger a linting error if <code>scale</code> is set (<a href="https://github.com/ewels/MultiQC/pull/2176">#2176</a>)</li> <li>Stricter config variable typing (<a href="https://github.com/ewels/MultiQC/pull/2178">#2178</a>)</li> <li>Remove <code>position:absolute</code> CSS from table values (<a href="https://github.com/ewels/MultiQC/pull/2169">#2169</a>)</li> <li>Fix column sorting in exported TSV files from a matplotlib linegraph plot (<a href="https://github.com/ewels/MultiQC/pull/2143">#2143</a>)</li> <li>Fix custom anchors for kraken (<a href="https://github.com/ewels/MultiQC/pull/2170">#2170</a>)</li> <li>Fix logging spillover bug (<a href="https://github.com/ewels/MultiQC/pull/2174">#2174</a>)</li> </ul> <h2>New Modules</h2> <ul> <li><a href="https://github.com/seqeralabs/tower-cli"><strong>Seqera Platform CLI</strong></a> (<a href="https://github.com/ewels/MultiQC/pull/2151">#2151</a>)<ul> <li>Seqera Platform CLI reports statistics generated by the Seqera Platform CLI.</li> </ul> </li> <li><a href="https://github.com/data61/gossamer/blob/master/docs/xenome.md"><strong>Xenome</strong></a> (<a href="https://github.com/ewels/MultiQC/pull/1860">#1860</a>)<ul> <li>A tool for classifying reads from xenograft sources.</li> </ul> </li> <li><a href="https://gitlab.com/genomeinformatics/xengsort"><strong>xengsort</strong></a> (<a href="https://github.com/ewels/MultiQC/pull/2168">#2168</a>)<ul> <li>xengsort is a fast xenograft read sorter based on space-efficient k-mer hashing</li> </ul> </li> </ul> <h2>Module updates</h2> <ul> <li><strong>fastp</strong>: add version parsing (<a href="https://github.com/ewels/MultiQC/pull/2159">#2159</a>)</li> <li><strong>fastp</strong>: correctly parse sample name from <code>--in1</code>/<code>--in2</code> in bash command. Prefer file name if not <code>fastp.json</code>; fallback to file name when error (<a href="https://github.com/ewels/MultiQC/pull/2139">#2139</a>)</li> <li><strong>Kaiju</strong>: fix <code>division by zero</code> error (<a href="https://github.com/ewels/MultiQC/pull/2179">#2179</a>)</li> <li><strong>Nanostat</strong>: account for both tab and spaces in <code>v1.41+</code> search pattern (<a href="https://github.com/ewels/MultiQC/pull/2155">#2155</a>)</li> <li><strong>Pangolin</strong>: update for v4: add QC Note , update tool versions columns (<a href="https://github.com/ewels/MultiQC/pull/2157">#2157</a>)</li> <li><strong>Picard</strong>: Generalize to directly support Sentieon and Parabricks outputs (<a href="https://github.com/ewels/MultiQC/pull/2110">#2110</a>)</li> <li><strong>Sentieon</strong>: Removed the module in favour of directly supporting parsing by the <strong>Picard</strong> module (<a href="https://github.com/ewels/MultiQC/pull/2110">#2110</a>)<ul> <li>Note that any code that relies on the module name needs to be updated, e.g. <code>-m sentieon</code> will no longer work</li> <li>The exported plot and data files will be now be prefixed as <code>picard</code> instead of <code>sentieon</code>, etc.</li> <li>Note that the Sentieon module used to fetch the sample names from the file names by default, and now it follows the Picard module's logic, and prioritizes the commands recorded in the logs. To override, use the <code>use_filename_as_sample_name</code> config flag</li> </ul> </li> </ul>Please consider citing MultiQC if you use it in your analysis

HiCUP: pipeline for mapping and processing Hi-C data.

HiCUP is a pipeline for processing sequence data generated by Hi-C and Capture Hi-C (CHi-C) experiments, which are techniques used to investigate three-dimensional genomic organisation. The pipeline maps data to a specified reference genome and removes artefacts that would otherwise hinder subsequent analysis. HiCUP also produces an easy-to-interpret yet detailed quality control (QC) report that assists in refining experimental protocols for future studies. The software is freely available and has already been used for processing Hi-C and CHi-C data in several recently published peer-reviewed studies