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MotivationWith Next Generation Sequencing becoming more affordable every year, NGS technologies asserted themselves as the fastest and most reliable way to detect Single Nucleotide Variants (SNV) and Copy Number Variations (CNV) in cancer patients. These technologies can be used to sequence DNA at very high depths thus allowing to detect abnormalities in tumor cells with very low frequencies. A lot of different variant callers are publicly available and usually do a good job at calling out variants. However, when frequencies begin to drop under 1%, the specificity of these tools suffers greatly as true variants at very low frequencies can be easily confused with sequencing or PCR artifacts. The recent use of Unique Molecular Identifiers (UMI) in NGS experiments offered a way to accurately separate true variants from artifacts. UMI-based variant callers are slowly replacing raw-reads based variant callers as the standard method for an accurate detection of variants at very low frequencies. However, benchmarking done in the tools publication are usually realized on real biological data in which real variants are not known, making it difficult to assess their accuracy.ResultsWe present UMI-Gen, a UMI-based reads simulator for targeted sequencing paired-end data. UMI-Gen generates reference reads covering the targeted regions at a user customizable depth. After that, using a number of control files, it estimates the background error rate at each position and then modifies the generated reads to mimic real biological data. Finally, it will insert real variants in the reads from a list provided by the user.AvailabilityThe entire pipeline is available athttps://gitlab.com/vincent-sater/umigen-masterunder MIT license.Contactvincent.sater@gmail.com

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.

Next Generation Sequencing (NGS) has become the go-to standard method for the detection of Single Nucleotide Variants (SNV) 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 artifactual 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. 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 don9t 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.

With the advent of high-throughput sequencing technologies there are more and more genomic sequences of individuals of the same species available. These sequences only differ by a very small amount of variations. There is thus a strong need for efficient algorithms for performing fast pattern matching in such specific sets of sequences. In this paper we propose efficient practical algorithms that solve on-line exact pattern matching problem in a set of highly similar DNA sequences. We first present a method for exact single pattern matching when $k$ variations are allowed in a window which size is equal to the pattern length. We then propose an algorithm for exact multiple pattern matching when only one variation is allowed in a window which size is equal to the length of the longest pattern. Experimental results show that our algorithms, though not optimal in the worst case, have good performances in practice.

International audience; With the advent of high-throughput sequencing technologies there are more and more genomic sequences of individuals of the same species available. These sequences only differ by a very small amount of variations. There is thus a strong need for efficient algorithms for performing fast pattern matching in such specific sets of sequences. In this paper, we propose efficient practical algorithms that solve on-line exact pattern matching problem in a set of highly similar DNA sequences. We first present a method for exact single pattern matching when k variations are allowed in a window which size is equal to the pattern length. We then propose an algorithm for exact multiple pattern matching when only one variation is allowed in a window which size is equal to the length of the longest pattern. Experimental results show that our algorithms, though not optimal in the worst case, have good performances in practice.

Background: Medical text repositories not only constitute a significant amount of data but also represent an interesting scientific test bed for those willing to apply natural language processing to information retrieval. In order to improve retrieval performance of the Catalogue and Index of Health Resources in French (CISMeF) and its search tool Doc’CISMeF, we tested a new method to correct misspellings of the queries written by the users. Methods: In addition to exact phonetic term matching, we tested two approximate string comparators. The approximate comparators are the string distance metric of Stoilos and the Levenshtein edit distance. We also calculated the results of the two-combined algorithm to examine whether it improves misspelling correction of the queries. Results: At a threshold comparator score of 0.2, the normalized Levenshtein algorithm achieved the highest recall of 76% but the highest precision 94% is achieved by combining the distances of Levenshtein and Stoilos. Conclusion: Although the well-known good performance of the normalized edit distance of Levenshtein, we have demonstrated in this paper that its combination with the Stoilos algorithm improves the results for misspelling correction.

International audience; Background: Most of the institutional and research information in the biomedical domain is available in the formof English text. Even in countries where English is an official language, such as the United States, language can be abarrier for accessing biomedical information for non-native speakers. Recent progress in machine translationsuggests that this technique could help make English texts accessible to speakers of other languages. However, thelack of adequate specialized corpora needed to train statistical models currently limits the quality of automatictranslations in the biomedical domain.Results: We show how a large-sized parallel corpus can automatically be obtained for the biomedical domain,using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINEand abstract text automatically retrieved from journal websites, which substantially extends the corpora used inprevious work. After assessing the quality of the corpus for two language pairs (English/French and English/Spanish)we use the Moses package to train a statistical machine translation model that outperforms previous models forautomatic translation of biomedical text.Conclusions: We have built translation data sets in the biomedical domain that can easily be extended to otherlanguages available in MEDLINE. These sets can successfully be applied to train statistical machine translationmodels. While further progress should be made by incorporating out-of-domain corpora and domain-specificlexicons, we believe that this work improves the automatic translation of biomedical texts.

Background The Internet is a major source of health information but most seekers are not familiar with medical vocabularies. Hence, their searches fail due to bad query formulation. Several methods have been proposed to improve information retrieval: query expansion, syntactic and semantic techniques or knowledge-based methods. However, it would be useful to clean those queries which are misspelled. In this paper, we propose a simple yet efficient method in order to correct misspellings of queries submitted by health information seekers to a medical online search tool. Methods In addition to query normalizations and exact phonetic term matching, we tested two approximate string comparators: the similarity score function of Stoilos and the normalized Levenshtein edit distance. We propose here to combine them to increase the number of matched medical terms in French. We first took a sample of query logs to determine the thresholds and processing times. In the second run, at a greater scale we tested different combinations of query normalizations before or after misspelling correction with the retained thresholds in the first run. Results According to the total number of suggestions (around 163, the number of the first sample of queries), at a threshold comparator score of 0.3, the normalized Levenshtein edit distance gave the highest F-Measure (88.15%) and at a threshold comparator score of 0.7, the Stoilos function gave the highest F-Measure (84.31%). By combining Levenshtein and Stoilos, the highest F-Measure (80.28%) is obtained with 0.2 and 0.7 thresholds respectively. However, queries are composed by several terms that may be combination of medical terms. The process of query normalization and segmentation is thus required. The highest F-Measure (64.18%) is obtained when this process is realized before spelling-correction. Conclusions Despite the widely known high performance of the normalized edit distance of Levenshtein, we show in this paper that its combination with the Stoilos algorithm improved the results for misspelling correction of user queries. Accuracy is improved by combining spelling, phoneme-based information and string normalizations and segmentations into medical terms. These encouraging results have enabled the integration of this method into two projects funded by the French National Research Agency-Technologies for Health Care. The first aims to facilitate the coding process of clinical free texts contained in Electronic Health Records and discharge summaries, whereas the second aims at improving information retrieval through Electronic Health Records.