Rafael Larrosa

The recent emergence of long-read sequencing technologies has enabled substantial improvements in accuracy and reduced computational costs. Nonetheless, pairwise sequence alignment remains a time-consuming step in common bioinformatics pipelines, becoming a bottleneck in de novo whole-genome assembly. Speeding up this step requires heuristics and the development of memory-frugal and efficient implementations. A promising candidate for all of the above is Myers’ algorithm. However, the state-of-the-art implementations face scalability challenges when dealing with longer reads and large datasets. To address these challenges, we propose SeqMatcher, a fast and memory-frugal genomics sequence aligner. By leveraging the long registers of AVX-512, SeqMatcher reduces the data movement and memory footprint. In a comprehensive performance evaluation, SeqMatcher achieves speedups of up to 12.32x for the unbanded version and 26.70x for the banded version compared to the non-vectorized implementation, along with energy footprint reductions of up to 2.59x. It also outperforms state-of-the-art implementations by factors of up to 29.21x, 17.56x, 13.47x, 9.12x, and 8.81x compared to Edlib, WFA2-lib, SeqAn, BSAlign, and QuickEd, while improving energy consumption with reductions of up to 6.78x.

Lung cancer (LC) is the most common cause of cancer death worldwide mostly due to the low survival rate: 75% of cases are identified in advanced stages. In this study, the list of useful biomarkers to make an early diagnosis using liquid biopsies was expanded. A total of 30 samples of LC were analyzed to define potential miRNA biomarkers in liquid biopsies for LC. The biomarkers have been identified in interaction networks miRNA–mRNA. The potential biomarkers have been then validated in large cohorts. A total of 15 candidate miRNAs, that regulate the repression of 30 mRNAs, have been identified as a specific functional interaction network for squamous carcinoma, while the specific functional interaction network of adenocarcinoma consists of four candidate miRNAs that seem to handle the repression of five mRNA. Inspection of expression levels in larger cohorts validates the usefulness of the 11 candidates as biomarkers in liquid biopsies. The 11 candidate miRNAs found could be utilized to form diagnostic predictive biomarkers for LC in liquid biopsies.

Overview Lung cancer is one of the deadliest cancers in the world. Its histological classification depends on early diagnosis and successful treatment. Therefore, having specific biomarkers for a quick sorting widens the successful output of lung cancer treatment. Material and methods High-throughput sequencing (RNA-seq) was performed of small cohorts of BioBanco samples from healthy and tumour cells from lung adenocarcinoma (LUAD) and squamous cell carcinoma of the lung (lSCC). RNA-seq samples from small cell lung cancer (SCLC) were downloaded from databases. A bioinformatic workflow has been programmed for the identification of differentially expressed genes (DEGs). Results A total of 4777 DEGs were differentially expressed in SCLC, 3676 DEGs were in lSCC, while the lowest number of DEGs, 2819, appeared in LUAD. Among them, 945 DEGs were common to the three histological types. Once validated their expression profile and their survival predictive capacity in large, public cohorts, three DEGs can be exclusively considered as diagnostic biomarkers, three as prognosis biomarkers, and other three exhibit both diagnosis and prognosis capabilities. Conclusions This prospective study presents evidences for the diagnostic and prognostic capabilities of expression changes in CAPN8-2, TMC5 and MUC1 in LUAD, while they are non-significant in SCLC and lSCC. Their translation to clinical practice is proposed.

Gene expression analyses demand appropriate reference genes (RGs) for normalization, in order to obtain reliable assessments. Ideally, RG expression levels should remain constant in all cells, tissues or experimental conditions under study. Housekeeping genes traditionally fulfilled this requirement, but they have been reported to be less invariant than expected; therefore, RGs should be tested and validated for every particular situation. Microarray data have been used to propose new RGs, but only a limited set of model species and conditions are available; on the contrary, RNA-seq experiments are more and more frequent and constitute a new source of candidate RGs. An automated workflow based on mapped NGS reads has been constructed to obtain highly and invariantly expressed RGs based on a normalized expression in reads per mapped million and the coefficient of variation. This workflow has been tested with Roche/454 reads from reproductive tissues of olive tree (Olea europaea L.), as well as with Illumina paired-end reads from two different accessions of Arabidopsis thaliana and three different human cancers (prostate, small-cell cancer lung and lung adenocarcinoma). Candidate RGs have been proposed for each species and many of them have been previously reported as RGs in literature. Experimental validation of significant RGs in olive tree is provided to support the algorithm. Regardless sequencing technology, number of replicates, and library sizes, when RNA-seq experiments are designed and performed, the same datasets can be analyzed with our workflow to extract suitable RGs for subsequent PCR validation. Moreover, different subset of experimental conditions can provide different suitable RGs.