Biology subjects

Filion, G. J.

Publications and source records attributed to Filion, G. J..

7 recordsLinked to original sources

bioRxiv2026

A novel High-Throughput Ligase-Independent Mapping method to detect Viral Integration Sites

Integrated DNA elements are central to virology, functional genomics, and gene therapy, but current insertion-site mapping methods often rely on restriction digestion, ligation, or complex sequencing workflows that introduce bias and limit recovery. Here, we present Terminal Mapping, a high-throughput method that identifies host-insert junctions without restriction enzymes or DNA ligation. The workflow combines linear amplification from a known terminal sequence, enrichment of single-stranded products, terminal transferase-mediated poly-A tailing, and PCR amplification for Illumina or Oxford Nanopore sequencing. Applied to Jurkat T cells transduced with HIV-1- and SIVmac251-derived vectors, Terminal Mapping recovered more HIV-1 insertion sites than inverse PCR, reproduced known integration biases, and showed improved robustness with long-read sequencing. It revealed shared but quantitatively distinct HIV-1 and SIVmac251 hotspots, as well as substantial differences between Jurkat cell sources. Comparison of 5' and 3' LTR-derived reads also provided an internal control for unintegrated viral DNA. Terminal Mapping therefore offers a rapid and flexible platform for profiling integrated genetic elements across vectors and cellular contexts.

genomics↗

bioRxiv2024

CodonTransformer: a multispecies codon optimizer using context-aware neural networks

The genetic code is degenerate allowing a multitude of possible DNA sequences to encode the same protein. This degeneracy impacts the efficiency of heterologous protein production due to the codon usage preferences of each organism. The process of tailoring organism-specific synonymous codons, known as codon optimization, must respect local sequence patterns that go beyond global codon preferences. As a result, the search space faces a combinatorial explosion that makes exhaustive exploration impossible. Nevertheless, throughout the diverse life on Earth, natural selection has already optimized the sequences, thereby providing a rich source of data allowing machine learning algorithms to explore the underlying rules. Here, we introduce CodonTransformer, a multispecies deep learning model trained on over 1 million DNA-protein pairs from 164 organisms spanning all kingdoms of life. The model demonstrates context-awareness thanks to the attention mechanism and bidirectionality of the Transformers we used, and to a novel sequence representation that combines organism, amino acid, and codon encodings. CodonTransformer generates host-specific DNA sequences with natural-like codon distribution profiles and with negative cis-regulatory elements. This work introduces a novel strategy of Shared Token Representation and Encoding with Aligned Multi-masking (STREAM) and provides a state-of-the-art codon optimization framework with a customizable open-access model and a user-friendly interface.

synthetic biology↗

bioRxiv2020

Chromosome Compartments on the Inactive X Guide TAD Formation Independently of Transcription during X-Reactivation

A hallmark of chromosome organization is the partition into transcriptionally active A and repressed B compartments and into topologically associating domains (TADs). Both structures were regarded absent from the inactive X chromosome, but to be re-established with transcriptional reactivation and chromatin opening during X-reactivation. Here, we combine a tailor-made mouse iPSC-reprogramming system and high-resolution Hi-C to produce the first time-course combining gene reactivation, chromatin opening and chromosome topology during X-reactivation. Contrary to previous observations, we uncover A/B-like compartments on the inactive X harboring multiple subcompartments. While partial X-reactivation initiates within a compartment rich in X-inactivation escapees, it then occurs rapidly along the chromosome, coinciding with acquisition of naive pluripotency, leading to downregulation of Xist. Importantly, we find that TAD formation precedes transcription, suggesting them to be causally independent. Instead, TADs form first within Xist-poor compartments, establishing Xist as common denominator, opposing both gene reactivation and TAD formation through separate mechanisms. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/177790v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1e5ba28org.highwire.dtl.DTLVardef@120ad95org.highwire.dtl.DTLVardef@64264borg.highwire.dtl.DTLVardef@10e168a_HPS_FORMAT_FIGEXP M_FIG Graphical Summary C_FIG

genomics↗

bioRxiv2020

Mapping short reads, faithfully

Mapping is the process of finding the original location of a DNA read in a reference sequence, typically a genome. Short read mappers are software tools used in most applications that involve high-throughput sequencing. As such, they must be continuously improved to keep up with increasing needs. Modern mappers rely on seeding heuristics, making them fast but inexact. For lack of a method to compute the reliability of their own output, mappers have so far used approximations of variable quality. Here we focus on faithfulness, the capacity to provide accurate mapping confidence, and we devise a strategy to map short reads faithfully. The key is to estimate the repetitiveness of the target reference, which is the dominant factor for the reliability of the mapping process. This approach highlights the existence of a class of reads that can be mapped with unprecedented confidence. We exploit this strategy in a prototype mapper that is competitive with state-of-the-art mappers BWA-MEM and Bowtie2, with the benefit of faithfulness. The software is open-source and available for download at https://github.com/gui11aume/mmp.

bioinformatics↗

bioRxiv2019

Strand asymmetry influences mismatch repair during single-strand annealing

BackgroundBiases of DNA repair can shape the nucleotide landscape of genomes at evolutionary timescales. However, such biases have not yet been measured in chromatin for lack of technologies. ResultsHere we develop a genome-wide assay whereby the same DNA lesion is repaired in different chromatin contexts. We insert thousands of barcoded transposons carrying a reporter of DNA mismatch repair in the genome of mouse embryonic stem cells. Upon inducing a double-strand break between tandem repeats, a mismatch is generated when the single strand annealing repair pathway is used. Regardless of the mismatch, we observed a 60-80% bias in the resolution in favor of one strand. The location of the lesion in the genome and the type of mismatch had little influence on the bias in this context. Instead, changing the position of the double-strand break in the reporter gave a complete reversion of the bias. ConclusionThese results suggest that the processing of the double-strand break has a major influence on the repair of mismatches during single-strand annealing, irrespective of the surrounding chromatin context.

genomics↗

bioRxiv2019

Calibrating seed-based heuristics to map short DNA reads

The increasing throughput of DNA sequencing technologies creates a need for faster algorithms. The fate of most reads is to be mapped to a reference sequence, typically a genome. Modern mappers rely on heuristics to gain speed at a reasonable cost for accuracy. In the seeding heuristic, short matches between the reads and the genome are used to narrow the search to a set of candidate locations. Several seeding variants used in modern mappers show good empirical performance but they are difficult to calibrate or to optimize for lack of theoretical results. Here we develop a theory to estimate the probability that the correct location of a read is filtered out during seeding, resulting in mapping errors. We describe the properties of simple exact seeds, skip-seeds and MEM seeds (Maximal Exact Match seeds). The main innovation of this work is to use concepts from analytic combinatorics to represent reads as abstract sequences, and to specify their generative function to estimate the probabilities of interest. We provide several algorithms, which combined together give a workable solution for the problem of calibrating seeding heuristics for short reads. We also provide a C implementation of these algorithms in a library called Sesame. These results can improve current mapping algorithms and lay the foundation of a general strategy to tackle sequence alignment problems. The Sesame library is open source and available for download at https://github.com/gui11aume/sesame.

bioinformatics↗

bioRxiv2017

Analytic combinatorics for bioinformatics I: seeding methods

Seeding heuristics are the most widely used strategies to speed up sequence alignment in bioinformatics. Such strategies are most successful if they are calibrated, so that the speed-versus-accuracy trade-off can be properly tuned. In the widely used case of read mapping, it has been so far impossible to predict the success rate of competing seeding strategies for lack of a theoretical framework. Here I present an approach to estimate such quantities based on the theory of analytic combinatorics. In a nutshell, the strategy is to specify a combinatorial construction of reads where the seeding heuristic fails, translate this specification into a generating function using formal rules, and finally extract the probabilities of interest from the singularities of the generating function. I use this approach to construct simple estimators of the success rate of the seeding heuristic under different types of sequencing errors. I also show how the analytic combinatorics strategy can be used to compute the associated type I and type II error rates (mapping the read to the wrong location, or being unable to map the read). Finally, I show how analytic combinatorics can be used to estimate average quantities such as the expected number of errors in reads where the seeding heuristic fails. Overall, this work introduces a theoretical and practical framework to find the success rate of seeding heuristics and related problems in bioinformatics.

bioinformatics↗