arXiv subjects

Stefano Lonardi

Publications and source records attributed to Stefano Lonardi.

4 recordsLinked to original sources

arXiv2026

AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2

Machine learning-based antibody design is emerging as one of the most promising approaches to combat infectious diseases, due to significant advancements in the field of artificial intelligence and an exponential surge in experimental antibody data (in particular related to COVID-19). The ability of an antibody to bind to an antigens (called binding affinity) is one of the the most critical properties in designing neutralizing antibodies. In this study we introduce Ab-Affinity, a new large language model that can accurately predict the binding affinity of antibodies against a target peptide, e.g., the SARS-CoV-2 spike protein. Code and model are available at https://github.com/ucrbioinfo/AbAffinity.

q-bio.QM↗

arXiv2016

RasBhari: optimizing spaced seeds for database searching, read mapping and alignment-free sequence comparison

Many algorithms for sequence analysis rely on word matching or word statistics. Often, these approaches can be improved if binary patterns representing match and don't-care positions are used as a filter, such that only those positions of words are considered that correspond to the match positions of the patterns. The performance of these approaches, however, depends on the underlying patterns. Herein, we show that the overlap complexity of a pattern set that was introduced by Ilie and Ilie is closely related to the variance of the number of matches between two evolutionarily related sequences with respect to this pattern set. We propose a modified hill-climbing algorithm to optimize pattern sets for database searching, read mapping and alignment-free sequence comparison of nucleic-acid sequences; our implementation of this algorithm is called rasbhari. Depending on the application at hand, rasbhari can either minimize the overlap complexity of pattern sets, maximize their sensitivity in database searching or minimize the variance of the number of pattern-based matches in alignment-free sequence comparison. We show that, for database searching, rasbhari generates pattern sets with slightly higher sensitivity than existing approaches. In our Spaced Words approach to alignment-free sequence comparison, pattern sets calculated with rasbhari led to more accurate estimates of phylogenetic distances than the randomly generated pattern sets that we previously used. Finally, we used rasbhari to generate patterns for short read classification with CLARK-S. Here too, the sensitivity of the results could be improved, compared to the default patterns of the program. We integrated rasbhari into Spaced Words; the source code of rasbhari is freely available at http://rasbhari.gobics.de/

q-bio.GN↗

arXiv2013

Accurate Decoding of Pooled Sequenced Data Using Compressed Sensing

In order to overcome the limitations imposed by DNA barcoding when multiplexing a large number of samples in the current generation of high-throughput sequencing instruments, we have recently proposed a new protocol that leverages advances in combinatorial pooling design (group testing) doi:10.1371/journal.pcbi.1003010. We have also demonstrated how this new protocol would enable de novo selective sequencing and assembly of large, highly-repetitive genomes. Here we address the problem of decoding pooled sequenced data obtained from such a protocol. Our algorithm employs a synergistic combination of ideas from compressed sensing and the decoding of error-correcting codes. Experimental results on synthetic data for the rice genome and real data for the barley genome show that our novel decoding algorithm enables significantly higher quality assemblies than the previous approach.

q-bio.QM↗

arXiv2011

Barcoding-free BAC Pooling Enables Combinatorial Selective Sequencing of the Barley Gene Space

We propose a new sequencing protocol that combines recent advances in combinatorial pooling design and second-generation sequencing technology to efficiently approach de novo selective genome sequencing. We show that combinatorial pooling is a cost-effective and practical alternative to exhaustive DNA barcoding when dealing with hundreds or thousands of DNA samples, such as genome-tiling gene-rich BAC clones. The novelty of the protocol hinges on the computational ability to efficiently compare hundreds of million of short reads and assign them to the correct BAC clones so that the assembly can be carried out clone-by-clone. Experimental results on simulated data for the rice genome show that the deconvolution is extremely accurate (99.57% of the deconvoluted reads are assigned to the correct BAC), and the resulting BAC assemblies have very high quality (BACs are covered by contigs over about 77% of their length, on average). Experimental results on real data for a gene-rich subset of the barley genome confirm that the deconvolution is accurate (almost 70% of left/right pairs in paired-end reads are assigned to the same BAC, despite being processed independently) and the BAC assemblies have good quality (the average sum of all assembled contigs is about 88% of the estimated BAC length).

q-bio.GN↗