Persistent minimal sequences of SARS-CoV-2

Novel Minimal Absent Words Detected in Influenza A Virus

Influenza is a communicable disease caused by RNA viruses. Strains A (affecting animals, humans), B (affecting humans), C (affecting rarely humans and pigs), and D (affecting cattle) comprise a variety of substrains each. Influenza A strain, affecting both humans and animals, is considered the most infectious, causing pandemics. There is an emerging need for the accurate classification of the different influenza A virus (IAV) subtypes, elucidating their mode of infection, as well as their fast and accurate diagnosis. Notably, in recent years, oligomeric sequences (words) that are present in the pathogen genomes and entirely absent from the host human genome were suggested to provide robust biomarkers for virus classification and rapid detection. To this end, we performed updated phylogenetic analyses of the IAV hemagglutinin genes, focusing on the sub H1N1 and H5N1. More importantly, we applied in silico methods to identify minimum length "words" that exist consistently in the IAV genomes and are entirely absent from the human genome; these sequences identified in our current analysis may represent minimal signatures that can be utilized to distinguish IAV from other influenza viruses, as well as to perform rapid diagnostic tests.

The topography of nullomer-emerging mutations and their relevance to human disease

Nullomers are short DNA sequences (11-18 base pairs) that are absent from a genome; however, they can emerge due to mutations. Here, we characterize all possible putative human nullomer-emerging single base pair mutations, population variants and disease-causing mutations. We find that the primary determinants of nullomer emergence in the human genome are the presence of CpG dinucleotides and methylated cytosines. Putative nullomer-emerging mutations are enriched at specific genomic elements, including transcription start and end sites, splice sites and transcription factor binding sites. We also observe that putative nullomer-emerging mutations are more frequent in highly conserved regions and show preferential location at nucleosomes. Among repeat elements, Alu repeats exhibit pronounced enrichment for putative nullomer-emerging mutations at specific positions. Finally, we find that disease-associated pathogenic mutations are significantly more likely to cause emergence of nullomers than their benign counterparts.

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

kmerDB: A database encompassing the set of genomic and proteomic sequence information for each species

The decrease in sequencing expenses has facilitated the creation of reference genomes and proteomes for an expanding array of organisms. Nevertheless, no established repository that details organism-specific genomic and proteomic sequences of specific lengths, referred to as kmers, exists to our knowledge. In this article, we present kmerDB, a database accessible through an interactive web interface that provides kmer-based information from genomic and proteomic sequences in a systematic way. kmerDB currently contains 202,340,859,107 base pairs and 19,304,903,356 amino acids, spanning 54,039 and 21,865 reference genomes and proteomes, respectively, as well as 6,905,362 and 149,305,183 genomic and proteomic species-specific sequences, termed quasi-primes. Additionally, we provide access to 5,186,757 nucleic and 214,904,089 peptide sequences absent from every genome and proteome, termed primes. kmerDB features a user-friendly interface offering various search options and filters for easy parsing and searching. The service is available at: www.kmerdb.com.

AltaiR: a C toolkit for alignment-free and temporal analysis of multi-FASTA data

BACKGROUND

Most viral genome sequences generated during the latest pandemic have presented new challenges for computational analysis. Analyzing millions of viral genomes in multi-FASTA format is computationally demanding, especially when using alignment-based methods. Most existing methods are not designed to handle such large datasets, often requiring the analysis to be divided into smaller parts to obtain results using available computational resources.

FINDINGS

We introduce AltaiR, a toolkit for analyzing multiple sequences in multi-FASTA format using exclusively alignment-free methodologies. AltaiR enables the identification of singularity and similarity patterns within sequences and computes static and temporal dynamics without restrictions on the number or size of input sequences. It automatically filters low-quality, biased, or deviant data. We demonstrate AltaiR's capabilities by analyzing more than 1.5 million full severe acute respiratory virus coronavirus 2 sequences, revealing interesting observations regarding viral genome characteristics over time, such as shifts in nucleotide composition, decreases in average Kolmogorov sequence complexity, and the evolution of the smallest sequences not found in the human host.

CONCLUSIONS

AltaiR can identify temporal characteristics and trends in large numbers of sequences, making it ideal for scenarios involving endemic or epidemic outbreaks with vast amounts of available sequence data. Implemented in C with multithreading and methodological optimizations, AltaiR is computationally efficient, flexible, and dependency-free. It accepts any sequence in FASTA format, including amino acid sequences. The complete toolkit is freely available at https://github.com/cobilab/altair.

K-mer applied in Mycobacterium tuberculosis genome cluster analysis

Abstract According to studies carried out, approximately 10 million people developed tuberculosis in 2018. Of this total, 1.5 million people died from the disease. To study the behavior of the genome sequences of Mycobacterium tuberculosis (MTB), the bacterium responsible for the development of tuberculosis (TB), an analysis was performed using k-mers (DNA word frequency). The k values ranged from 1 to 10, because the analysis was performed on the full length of the sequences, where each sequence is composed of approximately 4 million base pairs, k values above 10, the analysis is interrupted, as consequence of the program's capacity. The aim of this work was to verify the formation of the phylogenetic tree in each k-mer analyzed. The results showed the formation of distinct groups in some k-mers analyzed, taking into account the threshold line. However, in all groups, the multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains remained together and separated from the other strains.

Significant non-existence of sequences in genomes and proteomes

Minimal absent words (MAWs) are minimal-length oligomers absent from a genome or proteome. Although some artificially synthesized MAWs have deleterious effects, there is still a lack of a strategy for the classification of non-occurring sequences as potentially malicious or benign. In this work, by using Markovian models with multiple-testing correction, we reveal significant absent oligomers, which are statistically expected to exist. This suggests that their absence is due to negative selection. We survey genomes and proteomes covering the diversity of life and find thousands of significant absent sequences. Common significant MAWs are often mono- or dinucleotide tracts, or palindromic. Significant viral MAWs are often restriction sites and may indicate unknown restriction motifs. Surprisingly, significant mammal genome MAWs are often present, but rare, in other mammals, suggesting that they are suppressed but not completely forbidden. Significant human MAWs are frequently present in prokaryotes, suggesting immune function, but rarely present in human viruses, indicating viral mimicry of the host. More than one-fourth of human proteins are one substitution away from containing a significant MAW, with the majority of replacements being predicted harmful. We provide a web-based, interactive database of significant MAWs across genomes and proteomes.