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PubMed2025

In silico generation of synthetic cancer genomes using generative AI.

Understanding how genomic alterations drive cancer is key to advancing precision oncology. To detect these alterations, accurate algorithms are used; however, due to privacy concerns, few deeply sequenced cancer genomes can be shared, limiting benchmarking and representing a major obstacle to the improvement of analytic tools. To address this, we developed OncoGAN, a generative AI model combining adversarial networks and variational autoencoders to create realistic synthetic cancer genomes. Trained on large-scale genomic datasets, OncoGAN accurately reproduces somatic mutations, copy number alterations, and structural variants across cancer types while preserving donors' privacy. The synthetic genomes reflect tumor-specific mutational signatures and positional mutation patterns. Using DeepTumour, we validated the synthetic data's fidelity, showing high concordance between generated and predicted tumors. Moreover, augmenting the training data with synthetic genomes improved DeepTumour's accuracy, underscoring OncoGAN's potential to generate shareable datasets with known ground truths for benchmarking and enhancement of cancer genome analysis tools.

Humans↗

PubMed2002

Reliability of transmembrane predictions in whole-genome data.

Transmembrane prediction methods are generally benchmarked on a set of proteins with experimentally verified topology. We have investigated if the accuracy measured on such datasets can be expected in an unbiased genomic analysis, or if there is a bias towards 'easily predictable' proteins in the benchmark datasets. As a measurement of accuracy, the concordance of the results from five different prediction methods was used (TMHMM, PHD, HMMTOP, MEMSAT, and TOPPRED). The benchmark dataset showed significantly higher levels (up to five times) of agreement between different methods than in 10 tested genomes. We have also analyzed which programs are most prone to make mispredictions by measuring the frequency of one-out-of-five disagreeing predictions.

Algorithms↗

PubMed2003

A complexity reduction algorithm for analysis and annotation of large genomic sequences.

DNA is a universal language encrypted with biological instruction for life. In higher organisms, the genetic information is preserved predominantly in an organized exon/intron structure. When a gene is expressed, the exons are spliced together to form the transcript for protein synthesis. We have developed a complexity reduction algorithm for sequence analysis (CRASA) that enables direct alignment of cDNA sequences to the genome. This method features a progressive data structure in hierarchical orders to facilitate a fast and efficient search mechanism. CRASA implementation was tested with already annotated genomic sequences in two benchmark data sets and compared with 15 annotation programs (10 ab initio and 5 homology-based approaches) against the EST database. By the use of layered noise filters, the complexity of CRASA-matched data was reduced exponentially. The results from the benchmark tests showed that CRASA annotation excelled in both the sensitivity and specificity categories. When CRASA was applied to the analysis of human Chromosomes 21 and 22, an additional 83 potential genes were identified. With its large-scale processing capability, CRASA can be used as a robust tool for genome annotation with high accuracy by matching the EST sequences precisely to the genomic sequences.

Algorithms↗

PubMed1996

Comparative analysis of 1196 orthologous mouse and human full-length mRNA and protein sequences.

A large set of mRNA and encoded protein sequences, from orthologous murine and human genes, was compiled to analyze statistical, biological, and evolutionary properties of coding and noncoding transcribed sequences. Protein sequence conservation varied between 36% and 100% identity, with an average value of 85%. The average degree of nucleotide sequence identity for the corresponding coding sequences was also approximately 85%, whereas 5' and 3' untranslated regions (UTRs) were less conserved, with aligned identities of 67% and 69%, respectively. For some mouse and human genes, nucleotide sequences are more highly conserved than the encoded protein sequences. A subset of 32 sequences, consisting of only mouse/human protein pairs for which the human sequence represents a positionally cloned disease gene, had properties very similar to the larger data set, suggesting that our data are representative of the genome as a whole. With respect to sequence conservation, two interesting outliers are the breast cancer (BRCAI) gene product and the testis-determining factor (SRY), both of which display among the lowest degrees of sequence identity. The occurrence of both introns and repetitive elements (e.g., Alu, Bl) in 5' and 3' UTRs was also studied. These results provide one benchmark for the "comparative genomics" of mice and humans, with practical implications for the cross-referencing of transcript maps. Also, they should prove useful in estimating the additional sampling diversity provided by mouse EST sequencing projects designed to complement the existing human cDNA collection.

Amino Acid Sequence↗

PubMed2026

PubMind: literature-based genetic variant extraction and functional annotation using large language models.

Biomedical literature contains extensive functional knowledge on genetic variants, but much remains inaccessible in unstructured text. Existing resources such as ClinVar and HGMD remain limited by coverage, submission bias, update frequency, and sparse annotation. We develop PubMind, an artificial intelligence (AI) framework that uses large language models (LLMs) to triage and extract variant-function-disease associations and supporting evidence from biomedical text. PubMind captures single-nucleotide, copy-number, structural, and gene-fusion variants, and normalizes records to genomic and transcriptomic coordinates. Benchmarking shows >90% accuracy for variant recognition and 99% precision for disease extraction. Applied to >41 million PubMed abstracts and >5 million full-text articles, PubMind generates PubMind-DB, a database of ~1.3 million unique variants with contextual annotations, accessible via web interface and API. Only ~10% of PubMind variants overlap with ClinVar, and >80% of them show concordant pathogenicity labels. PubMind transforms unstructured biomedical text into structured genomic knowledge, advancing variant interpretation for precision medicine.

Large Language Models↗

PubMed2025

Annotated genome of the Atlantic dog whelk, Nucella lapillus.

Nucella lapillus is an important player in rocky shore food chains and has been a focal organism of ecological and evolutionary studies for decades. Despite poor dispersal, they have a broad geographic range, which makes them an ideal species to examine isolation by distance and selection across environmental gradients. Here we present the fully annotated genome of N. lapillus generated with Oxford Nanopore Techonology sequencing at ∼37× coverage. The genome assembly is 2.32 Gbp and consists of 2,525 contigs, with an N50 length of 2 Mbp. Repeat annotation identified 2,491 families that cover 67.56% of the genome, which is similar to other gastropods. Despite its large size and high proportion of repeats, the genome is of high quality. Benchmarking Universal Single-Copy Ortholog (BUSCO) analysis revealed a score of 96.8%. Functional annotation of the genome produced 45,848 protein-coding genes with a 96.6% BUSCO score. Genomic resources for mollusks lag behind that of other phyla, perhaps because many of their innate characteristics complicate DNA extraction, sequencing, and assembly. This new N. lapillus genome will increase our genomic understanding of the second largest phylum (and the most diverse class within said phylum) and serve as a key resource to advance studies on the organismal biology and population genetics of this iconic species as well as the connection between genomic variation and community-level processes.

Animals↗

PubMed2026

SURE-Pipe: a pipeline to compare genomes and extract shared and unique regions.

Identification of unique and shared genomic regions between organisms has substantial translational potential for the development of marker-based diagnostic assays and sequence homology-driven taxonomic classification. An automated pipeline capable of performing genome comparisons at both the intra- and inter-species levels with minimal computational requirements can significantly advance genome-driven translational research. Species-specific genomic regions are particularly valuable for sequence-based species identification and for developing DNA amplification- or hybridization-based diagnostic assays. Here, we present SURE-Pipe, an automated and flexible pipeline for genome comparison and extraction of unique and shared genomic regions (https://github.com/BPaul-bioinfoLAB/SURE-Pipe). Benchmarking of this pipeline using simulated datasets demonstrated high accuracy for shared and unique region identification. Using the pairwise genome comparison module, six genome pairs from diverse microorganisms were analysed, and identified the unique and shared regions. In addition, the multigenome comparison module was applied to 96 genomes representing 24 Bacillus species and identified species-specific genomic regions. These regions were highly conserved among four strains of a species (>98% sequence identity) and exhibit little to no similarity with other species. Species-specific primers designed for all 24 Bacillus species showed no off-target amplification in in-silico polymerase chain reaction analysis, indicating their specificity. Overall, SURE-Pipe provides a robust and multipurpose framework for comparative genomics, and the outcomes can be used for species identification and the development of genome-based diagnostic approaches.

Genome, Bacterial↗

PubMed2003

Comparisons with Caenorhabditis (approximately 100 Mb) and Drosophila (approximately 175 Mb) using flow cytometry show genome size in Arabidopsis to be approximately 157 Mb and thus approximately 25% larger than the Arabidopsis genome initiative estimate of approximately 125 Mb.

Recent genome sequencing papers have given genome sizes of 180 Mb for Drosophila melanogaster Iso-1 and 125 Mb for Arabidopsis thaliana Columbia. The former agrees with early cytochemical estimates, but numerous cytometric estimates of around 170 Mb imply that a genome size of 125 Mb for arabidopsis is an underestimate. In this study, nuclei of species pairs were compared directly using flow cytometry. Co-run Columbia and Iso-1 female gave a 2C peak for arabidopsis only approx. 15 % below that for drosophila, and 16C endopolyploid Columbia nuclei had approx. 15 % more DNA than 2C chicken nuclei (with >2280 Mb). Caenorhabditis elegans Bristol N2 (genome size approx. 100 Mb) co-run with Columbia or Iso-1 gave a 2C peak for drosophila approx. 75 % above that for 2C C. elegans, and a 2C peak for arabidopsis approx. 57 % above that for C. elegans. This confirms that 1C in drosophila is approx. 175 Mb and, combined with other evidence, leads us to conclude that the genome size of arabidopsis is not approx. 125 Mb, but probably approx. 157 Mb. It is likely that the discrepancy represents extra repeated sequences in unsequenced gaps in heterochromatic regions. Complete sequencing of the arabidopsis genome until no gaps remain at telomeres, nucleolar organizing regions or centromeres is still needed to provide the first precise angiosperm C-value as a benchmark calibration standard for plant genomes, and to ensure that no genes have been missed in arabidopsis, especially in centromeric regions, which are clearly larger than once imagined.

Animals↗

PubMed2005

Genomic multiple sequence alignments: refinement using a genetic algorithm.

BACKGROUND: Genomic sequence data cannot be fully appreciated in isolation. Comparative genomics--the practice of comparing genomic sequences from different species--plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation) score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program. RESULTS: We tested the GenAlignRefine algorithm by running it on a Linux cluster to refine sequences from a simulation, as well as refine a multiple alignment of 15 Orthopoxvirus genomic sequences approximately 260,000 nucleotides in length that initially had been aligned by Multi-LAGAN. It took approximately 150 minutes for a 40-processor Linux cluster to optimize some 200 fuzzy (poorly aligned) regions of the orthopoxvirus alignment. Overall sequence identity increased only slightly; but significantly, this occurred at the same time that the overall alignment length decreased--through the removal of gaps--by approximately 200 gapped regions representing roughly 1,300 gaps. CONCLUSION: We have implemented a genetic algorithm in parallel mode to optimize multiple genomic sequence alignments initially generated by various alignment tools. Benchmarking experiments showed that the refinement algorithm improved genomic sequence alignments within a reasonable period of time.

Algorithms↗

PubMed1999

Functional insights from structural predictions: analysis of the Escherichia coli genome.

Fold assignments for proteins from the Escherichia coli genome are carried out using BASIC, a profile-profile alignment algorithm, recently tested on fold recognition benchmarks and on the Mycoplasma genitalium genome and PSI BLAST, the newest generation of the de facto standard in homology search algorithms. The fold assignments are followed by automated modeling and the resulting three-dimensional models are analyzed for possible function prediction. Close to 30% of the proteins encoded in the E. coli genome can be recognized as homologous to a protein family with known structure. Most of these homologies (23% of the entire genome) can be recognized both by PSI BLAST and BASIC algorithms, but the latter recognizes an additional 260 homologies. Previous estimates suggested that only 10-15% of E. coli proteins can be characterized this way. This dramatic increase in the number of recognized homologies between E. coli proteins and structurally characterized protein families is partly due to the rapid increase of the database of known protein structures, but mostly it is due to the significant improvement in prediction algorithms. Knowing protein structure adds a new dimension to our understanding of its function and the predictions presented here can be used to predict function for uncharacterized proteins. Several examples, analyzed in more detail in this paper, include the DPS protein protecting DNA from oxidative damage (predicted to be homologous to ferritin with iron ion acting as a reducing agent) and the ahpC/tsa family of proteins, which provides resistance to various oxidating agents (predicted to be homologous to glutathione peroxidase).

Algorithms↗

PubMed2025

A chromosomal level genome assembly of Nguni Sheep, Ovis aries.

Nguni sheep (Ovis aries) are indigenous to the Southern Africa region and common within the smallholder and poor resources farming systems. They are well adapted to different agroecological regions. However, limited genomic resources such as high-quality reference genomes have hindered our understanding of its adaptation and establishment of an effective breeding program. To address this, we assembled a chromosomal-level genome of Nguni sheep using a combination of PacBio HiFi reads and Omni-C reads. The genome size was estimated to be 2.9 Gb with a contig/scaffold N50 74 Mb and 99.6 Mb and a genome completeness of 96.1%, as estimated by the Benchmarking Universal Single-Copy Orthologs (BUSCO) program. The final genome encompassed a total of 25,926 protein-coding genes. The findings of this study provide a valuable genomic resource for understanding the adaptability of the Nguni sheep and the establishment of effective breeding programs.

Animals↗

PubMed2006

Phylogenetic reconstruction of orthology, paralogy, and conserved synteny for dog and human.

Accurate predictions of orthology and paralogy relationships are necessary to infer human molecular function from experiments in model organisms. Previous genome-scale approaches to predicting these relationships have been limited by their use of protein similarity and their failure to take into account multiple splicing events and gene prediction errors. We have developed PhyOP, a new phylogenetic orthology prediction pipeline based on synonymous rate estimates, which accurately predicts orthology and paralogy relationships for transcripts, genes, exons, or genomic segments between closely related genomes. We were able to identify orthologue relationships to human genes for 93% of all dog genes from Ensembl. Among 1:1 orthologues, the alignments covered a median of 97.4% of protein sequences, and 92% of orthologues shared essentially identical gene structures. PhyOP accurately recapitulated genomic maps of conserved synteny. Benchmarking against predictions from Ensembl and Inparanoid showed that PhyOP is more accurate, especially in its predictions of paralogy. Nearly half (46%) of PhyOP paralogy predictions are unique. Using PhyOP to investigate orthologues and paralogues in the human and dog genomes, we found that the human assembly contains 3-fold more gene duplications than the dog. Species-specific duplicate genes, or "in-paralogues," are generally shorter and have fewer exons than 1:1 orthologues, which is consistent with selective constraints and mutation biases based on the sizes of duplicated genes. In-paralogues have experienced elevated amino acid and synonymous nucleotide substitution rates. Duplicates possess similar biological functions for either the dog or human lineages. Having accounted for 2,954 likely pseudogenes and gene fragments, and after separating 346 erroneously merged genes, we estimated that the human genome encodes a minimum of 19,700 protein-coding genes, similar to the gene count of nematode worms. PhyOP is a fast and robust approach to orthology prediction that will be applicable to whole genomes from multiple closely related species. PhyOP will be particularly useful in predicting orthology for mammalian genomes that have been incompletely sequenced, and for large families of rapidly duplicating genes.

Animals↗

PubMed2003

THoR: a tool for domain discovery and curation of multiple alignments.

We describe a tool, THoR, that automatically creates and curates multiple sequence alignments representing protein domains. This exploits both PSI-BLAST and HMMER algorithms and provides an accurate and comprehensive alignment for any domain family. The entire process is designed for use via a web-browser, with simple links and cross-references to relevant information, to assist the assessment of biological significance. THoR has been benchmarked for accuracy using the SMART and pufferfish genome databases.

Algorithms↗

PubMed2006

Prophage Finder: a prophage loci prediction tool for prokaryotic genome sequences.

Prophage loci often remain under-annotated or even unrecognized in prokaryotic genome sequencing projects. A PHP application, Prophage Finder, has been developed and implemented to predict prophage loci, based upon clusters of phage-related gene products encoded within DNA sequences. This application provides results detailing several facets of these clusters to facilitate rapid prediction and analysis of prophage sequences. Prophage Finder was tested using previously annotated prokaryotic genomic sequences with manually curated prophage loci as benchmarks. Additional analyses from Prophage Finder searches of several draft prokaryotic genome sequences are available through the Web site (http://bioinformatics.uwp.edu/~phage/DOEResults.php) to illustrate the potential of this application.

Bacteria↗

PubMed2026

Comparative metagenomic assessment of Illumina-compatible library preparation methods, short-read lengths, and PacBio HiFi sequencing reveals differences in microbial and functional diversity recovery from a complex environmental sample.

UNLABELLED: Metagenomics enables comprehensive exploration of microbial communities but is influenced by library preparation and sequencing technologies, affecting recovery of microbial genomes and proteins. Here, we benchmarked six Illumina-compatible short-read library preparation conditions in triplicate at 2 × 150 bp and 2 × 250 bp read lengths alongside PacBio HiFi long-read sequencing using a composite environmental sample of marine mangrove sediment and terrestrial palm tree soil. Longer short reads (2 × 250 bp) combined with optimal library preparation approaches improved assembly quality, protein detection, and metagenome-assembled genome (MAG) recovery, achieving results approaching those of long-read sequencing. TruSeq libraries at 2 × 250 bp recovered more than sevenfold more unique proteins than the same kit at 2 × 150 bp (811,701 vs 110,108) using the same number of sequencing reads, while recovering a comparable number of high-quality MAGs to PacBio HiFi long-read sequencing (11 vs 18) and surpassing it in protein discovery by almost 10-fold (811,701 vs 87,745) at less than half of the sequencing cost. Furthermore, biosynthetic gene cluster analysis identified 46 biosynthetic gene clusters in TruSeq-250PE assemblies compared to 38 in PacBio HiFi, with several showing no close match in the MIBiG database. Although long reads yield more contiguity and complete genomes, longer short reads offer a cost-effective, scalable alternative for uncovering microbial and functional diversity. These findings provide critical guidance for metagenomic experimental design, demonstrating that strategic selection of library preparation chemistry and sequencing parameters can reveal more unknown microbial information in complex biomes without requiring additional sequencing depth. IMPORTANCE: Metagenomic outcomes are strongly influenced by library preparation and sequencing strategies, yet their combined effects in complex environmental samples remain poorly defined. Here, we provide the first direct comparison of Illumina NovaSeq short-read metagenomic sequencing at 2 × 150 bp and 2 × 250 bp across multiple library preparation kits, alongside PacBio HiFi long-read sequencing. We show that sequencing read length and library preparation critically shape assembly quality, protein recovery, and metagenome-assembled genome (MAG) reconstruction. These findings demonstrate that short-read sequencing at 2 × 250 bp, with appropriate library preparation, can match long-read technologies in MAG recovery while substantially surpassing them in protein discovery. With less than half of the sequencing price and a 3.5-fold reduction in cost per gigabase of usable data, this method facilitates more accessible large-scale metagenomic analysis within complex environmental systems.

Metagenomics↗

PubMed2006

Recognition of transmembrane segments in proteins: review and consistency-based benchmarking of internet servers.

Membrane proteins perform a number of crucial functions as transporters, receptors, and components of enzyme complexes. Identification of membrane proteins and prediction of their topology is thus an important part of genome annotation. We present here an overview of transmembrane segments in protein sequences, summarize data from large-scale genome studies, and report results of benchmarking of several popular internet servers.

Algorithms↗

PubMed2026

The 21st International ``Ponte di Legno'' Childhood Acute Lymphoblastic Leukemia Workshop Report: Progress and Emerging Opportunities.

The 21st Ponte di Legno Working Group meeting convened in Orlando, USA, on December 4-5, 2025, bringing together leading childhood acute lymphoblastic leukemia (ALL) investigators from major global consortia. In response to the transformative advances in childhood ALL treatment, particularly the integration of immunotherapy into frontline therapy, the group revisited and updated its mission statement. The revised mission emphasizes collaborative studies on rare leukemia subsets, harmonized toxicity reporting, particularly for immunotherapy-related toxicities, and unrestricted worldwide collaboration. In addition, sharing data and strategies for integrating novel agents will be integral to optimizing future trial design. Key scientific topics included rare genetic subgroups, T-cell ALL genomics, treatment-related toxicity benchmarking, and central nervous system (CNS) disease management challenges. A major focus was immunotherapy integration into frontline therapy, particularly blinatumomab as an emerging standard of care and inotuzumab ozogamicin as an investigational agent, and their potential to enable chemotherapy de-escalation. Additional discussions addressed immunotherapy-specific toxicities. The integration of immunotherapy into frontline ALL therapy represents a paradigm shift with potential to improve outcomes while reducing treatment burden. However, careful attention to CNS disease control, emerging toxicities, and preservation of the remarkable achievements in childhood ALL therapy remains essential as the field advances.

Blinatumomab↗

PubMed2026

FFC: a scalable FASTA compressor.

SUMMARY: FASTA is a widely used text-based format for storing nucleotide and protein sequences. The existing FASTA compressors usually focus on (slightly) improving the compression ratio, not on practical performance. We present FFC, a scalable FASTA compressor that achieves average compression speeds 4.7× and 11.4× higher than two high-performance compressors, zstd and NAF, respectively, across a benchmark set of seven single genomes. It also delivers average decompression speeds 3.5× and 2.7× higher than zstd and NAF, respectively. Although a chunk-based zstd variant with parallel decompression, pzstd, almost matches FFC speed, its compression ratio is on average by 23% worse than FFC's. For the experiment, a 14-core workstation and a RAM disk (to reduce the impact of I/O) were used. AVAILABILITY AND IMPLEMENTATION: FFC is freely available at github.com/kowallus/ffc and also as a Zenodo repository at 10.5281/zenodo.18892353, and the used datasets at 10.5281/zenodo.18873744.

Data Compression↗