UMI-Varcal: A Low-Frequency Variant Caller for UMI-Tagged Paired-End Sequencing Data.

The rapid transition from traditional sequencing methods to Next-Generation Sequencing (NGS) has allowed for a faster and more accurate detection of somatic variants (Single-Nucleotide Variant (SNV) and Copy Number Variation (CNV)) in tumor cells. NGS technologies require a succession of steps during which false variants can be silently added at low frequencies. Filtering these artifacts can be a rather difficult task especially when the experiments are designed to look for very low frequency variants. Recently, adding unique molecular barcodes called UMI (Unique Molecular Identifier) to the DNA fragments appears to be a very effective strategy to specifically filter out false variants from the variant calling results (Kukita et al. DNA Res 22(4):269-277, 2015; Newman et al. Nat Biotechnol 34(5):547-555, 2016; Schmitt et al. Proc Natl Acad Sci U S A 109(36):14508-14513). Here, we describe UMI-VarCal (Sater et al. Bioinformatics 36:2718-2724, 2020), which can use the UMI information from UMI-tagged reads to offer a faster and more accurate variant calling analysis.

Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers.

BACKGROUND: Recently, copy number variations (CNV) impacting genes involved in oncogenic pathways have attracted an increasing attention to manage disease susceptibility. CNV is one of the most important somatic aberrations in the genome of tumor cells. Oncogene activation and tumor suppressor gene inactivation are often attributed to copy number gain/amplification or deletion, respectively, in many cancer types and stages. Recent advances in next generation sequencing protocols allow for the addition of unique molecular identifiers (UMI) to each read. Each targeted DNA fragment is labeled with a unique random nucleotide sequence added to sequencing primers. UMI are especially useful for CNV detection by making each DNA molecule in a population of reads distinct. RESULTS: Here, we present molecular Copy Number Alteration (mCNA), a new methodology allowing the detection of copy number changes using UMI. The algorithm is composed of four main steps: the construction of UMI count matrices, the use of control samples to construct a pseudo-reference, the computation of log-ratios, the segmentation and finally the statistical inference of abnormal segmented breaks. We demonstrate the success of mCNA on a dataset of patients suffering from Diffuse Large B-cell Lymphoma and we highlight that mCNA results have a strong correlation with comparative genomic hybridization. CONCLUSION: We provide mCNA, a new approach for CNV detection, freely available at https://gitlab.com/pierrejulien.viailly/mcna/ under MIT license. mCNA can significantly improve detection accuracy of CNV changes by using UMI.

Large-scale comparative evaluation of user-friendly tools for predicting variant-induced alterations of splicing regulatory elements.

Discriminating which nucleotide variants cause disease or contribute to phenotypic traits remains a major challenge in human genetics. In theory, any intragenic variant can potentially affect RNA splicing by altering splicing regulatory elements (SREs). However, these alterations are often ignored mainly because pioneer SRE predictors have proved inefficient. Here, we report the first large-scale comparative evaluation of four user-friendly SRE-dedicated algorithms (QUEPASA, HEXplorer, SPANR, and HAL) tested both as standalone tools and in multiple combined ways based on two independent benchmark datasets adding up to >1,300 exonic variants studied at the messenger RNA level and mapping to 89 different disease-causing genes. These methods display good predictive power, based on decision thresholds derived from the receiver operating characteristics curve analyses, with QUEPASA and HAL having the best accuracies either as standalone or in combination. Still, overall there was a tight race between the four predictors, suggesting that all methods may be of use. Additionally, QUEPASA and HEXplorer may be beneficial as well for predicting variant-induced creation of pseudoexons deep within introns. Our study highlights the potential of SRE predictors as filtering tools for identifying disease-causing candidates among the plethora of variants detected by high-throughput DNA sequencing and provides guidance for their use in genomic medicine settings.

UMI-VarCal: a new UMI-based variant caller that efficiently improves low-frequency variant detection in paired-end sequencing NGS libraries.

MOTIVATION: Next-generation sequencing has become the go-to standard method for the detection of single-nucleotide variants in tumor cells. The use of such technologies requires a PCR amplification step and a sequencing step, steps in which artifacts are introduced at very low frequencies. These artifacts are often confused with true low-frequency variants that can be found in tumor cells and cell-free DNA. The recent use of unique molecular identifiers (UMI) in targeted sequencing protocols has offered a trustworthy approach to filter out artefactual variants and accurately call low-frequency variants. However, the integration of UMI analysis in the variant calling process led to developing tools that are significantly slower and more memory consuming than raw-reads-based variant callers. RESULTS: We present UMI-VarCal, a UMI-based variant caller for targeted sequencing data with better sensitivity compared to other variant callers. Being developed with performance in mind, UMI-VarCal stands out from the crowd by being one of the few variant callers that do not rely on SAMtools to do their pileup. Instead, at its core runs an innovative homemade pileup algorithm specifically designed to treat the UMI tags in the reads. After the pileup, a Poisson statistical test is applied at every position to determine if the frequency of the variant is significantly higher than the background error noise. Finally, an analysis of UMI tags is performed, a strand bias and a homopolymer length filter are applied to achieve better accuracy. We illustrate the results obtained using UMI-VarCal through the sequencing of tumor samples and we show how UMI-VarCal is both faster and more sensitive than other publicly available solutions. AVAILABILITY AND IMPLEMENTATION: The entire pipeline is available at https://gitlab.com/vincent-sater/umi-varcal-master under MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparative in depth RNA sequencing of P. tricornutum's morphotypes reveals specific features of the oval morphotype.

Phaeodactylum tricornutum is the most studied diatom encountered principally in coastal unstable environments. It has been hypothesized that the great adaptability of P. tricornutum is probably due to its pleomorphism. Indeed, P. tricornutum is an atypical diatom since it can display three morphotypes: fusiform, triradiate and oval. Currently, little information is available regarding the physiological significance of this morphogenesis. In this study, we adapted P. tricornutum Pt3 strain to obtain algal culture particularly enriched in one dominant morphotype: fusiform, triradiate or oval. These cultures were used to run high-throughput RNA-Sequencing. The whole mRNA transcriptome of each morphotype was determined. Pairwise comparisons highlighted biological processes and molecular functions which are up- and down-regulated. Finally, intersection analysis allowed us to identify the specific features from the oval morphotype which is of particular interest as it is often described to be more resistant to stresses. This study represent the first transcriptome wide characterization of the three morphotypes from P. tricornutum performed on cultures specifically enriched issued from the same Pt3 strain. This work represents an important step for the understanding of the morphogenesis in P. tricornutum and highlights the particular features of the oval morphotype.

EVA: Exome Variation Analyzer, an efficient and versatile tool for filtering strategies in medical genomics.

BACKGROUND: Whole exome sequencing (WES) has become the strategy of choice to identify a coding allelic variant for a rare human monogenic disorder. This approach is a revolution in medical genetics history, impacting both fundamental research, and diagnostic methods leading to personalized medicine. A plethora of efficient algorithms has been developed to ensure the variant discovery. They generally lead to ~20,000 variations that have to be narrow down to find the potential pathogenic allelic variant(s) and the affected gene(s). For this purpose, commonly adopted procedures which implicate various filtering strategies have emerged: exclusion of common variations, type of the allelics variants, pathogenicity effect prediction, modes of inheritance and multiple individuals for exome comparison. To deal with the expansion of WES in medical genomics individual laboratories, new convivial and versatile software tools have to implement these filtering steps. Non-programmer biologists have to be autonomous combining themselves different filtering criteria and conduct a personal strategy depending on their assumptions and study design. RESULTS: We describe EVA (Exome Variation Analyzer), a user-friendly web-interfaced software dedicated to the filtering strategies for medical WES. Thanks to different modules, EVA (i) integrates and stores annotated exome variation data as strictly confidential to the project owner, (ii) allows to combine the main filters dealing with common variations, molecular types, inheritance mode and multiple samples, (iii) offers the browsing of annotated data and filtered results in various interactive tables, graphical visualizations and statistical charts, (iv) and finally offers export files and cross-links to external useful databases and softwares for further prioritization of the small subset of sorted candidate variations and genes. We report a demonstrative case study that allowed to identify a new candidate gene related to a rare form of Alzheimer disease. CONCLUSIONS: EVA is developed to be a user-friendly, versatile, and efficient-filtering assisting software for WES. It constitutes a platform for data storage and for drastic screening of clinical relevant genetics variations by non-programmer geneticists. Thereby, it provides a response to new needs at the expanding era of medical genomics investigated by WES for both fundamental research and clinical diagnostics.

Identification and isolation of a pectin methylesterase isoform that could be involved in flax cell wall stiffening.

Pectin methylesterases (PMEs) are ubiquitous enzymes present in the plant cell wall. They catalyse the demethylesterification of homogalacturonic acid units of pectins, which, in turn, can be associated with different physiological phenomena. In this study, different flax (Linum usitatissimum L.) PME isoforms were observed: neutral (pI 7.0 and 7.5, MW: 110 kDa), basic (pI 8.3 and 8.5, MW: 110 kDa) and very basic (pI>9.5, MW: 38 kDa). In an attempt to identify most of the expressed cell wall LuPME isoforms, polyclonal antibodies were raised against a conserved region of PME. These antibodies allowed the purification of the very basic PME isoform (pI 9.5, MW: 36 kDa) from flax cells, designated LuPME5. This isoform corresponds to the Lupme5 cDNA isolated, at the same time, from flax hypocotyls, by using the RACE-PCR technique. Semi-quantitative PCR experiments showed that the Lupme5 transcript was highly expressed in the hypocotyl zones where elongation is being achieved. Thus, this enzyme may be involved in cell wall stiffening.