de Reynies, A. | bioRxiv Science

bioRxiv2018

SMAP: exploiting high-throughput sequencing data of patient derived xenografts

BackgroundPatient-derived xenograft is the model of reference in oncology fordrug response analyses. Xenografts samples have the specificity to be composedof cells from both the graft and the host species. Sequencing analysis ofxenograft samples therefore requires specific processing methods to properlyreconstruct genomic profiles of both the host and graft compartments.\n\nResultsWe propose a novel xenograft sequencing process pipeline termedSMAP for Simultaneous mapping. SMAP integrates the distinction of host andgraft sequencing reads to the mapping process by simultaneously aligning to bothgenome references. We show that SMAP increases accuracy of species-assignmentwhile reducing the number of discarded ambiguous reads compared to otherexisting methods. Moreover, SMAP includes a module called SMAP-fuz toimprove the detection of chimeric transcript fusion in xenograft RNAseq data. Finally, we apply SMAP on a real dataset and show the relevance of pathway andcell population analysis of the tumoral and stromal compartments.\n\nConclusionsIn high-throughput sequencing analysis of xenografts, our resultsshow that: i. the use of ad hoc sequence processing methods is essential, ii. highsequence homology does not introduce a significant bias when proper methodsare used and iii. the detection of fusion transcripts can be improved using ourapproach. SMAP is available on GitHub: cit-bioinfo.github.io/SMAP.

genomics↗

bioRxiv2018

Stabilized Independent Component Analysis outperforms other methods in finding reproducible signals in tumoral transcriptomes

MotivationMatrix factorization methods are widely exploited in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). Applying such methods to similar independent datasets should yield reproducible inter-series outputs, though it was never demonstrated yet.\n\nResultsWe systematically test state-of-art methods of matrix factorization on several transcriptomic datasets of the same cancer type. Inspired by concepts of evolutionary bioinformatics, we design a new framework based on Reciprocally Best Hit (RBH) graphs in order to benchmark the methods reproducibility. We show that a particular protocol of application of Independent Component Analysis (ICA), accompanied by a stabilisation procedure, leads to a significant increase in the inter-series output reproducibility. Moreover, we show that the signals detected through this method are systematically more interpretable than those of other state-of-art methods. We developed a user-friendly tool BIODICA for performing the Stabilized ICA-based RBH meta-analysis. We apply this methodology to the study of colorectal cancer (CRC) for which 14 independent publicly available transcriptomic datasets can be collected. The resulting RBH graph maps the landscape of interconnected factors that can be associated to biological processes or to technological artefacts. These factors can be used as clinical biomarkers or robust and tumor-type specific transcriptomic signatures of tumoral cells or tumoral microenvironment. Their intensities in different samples shed light on the mechanistic basis of CRC molecular subtyping.\n\nAvailabilityThe BIODICA tool is available from https://github.com/LabBandSB/BIODICA.\n\nContactlaura.cantini@curie.fr and andrei.zinovyev@curie.fr\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗