Suite of tools for statistical N-gram language modeling for pattern mining in whole genome sequences

A reference catalog of DNA palindromes in the human genome and their variations in 1000 Genomes

A palindrome in DNA is like a palindrome in language, but when read backwards, it is a complement of the forward sequence; effectively, the two halves of a sequence complement each other from its midpoint like in a double strand of DNA. Palindromes are distributed throughout the human genome and play significant roles in gene expression and regulation. Palindromic mutations are linked to many human diseases, such as neuronal disorders, mental retardation, and various cancers. In this work, we computed and analyzed the palindromic sequences in the human genome and studied their conservation in personal genomes using 1000 Genomes data. We found that ~30% of the palindromes exhibit variation, some of which are caused by rare variants. The analysis of disease/trait-associated single-nucleotide polymorphisms in palindromic regions showed that disease-associated risk variants are 14 times more likely to be present in palindromic regions than in other regions. The catalog of palindromes in the reference genome and 1000 Genomes is being made available here with details on their variations in each individual genome to serve as a resource for future and retrospective whole-genome studies identifying statistically significant palindrome variations associated with diseases or traits and their roles in disease mechanisms.

A pilot study on the prevalence of DNA palindromes in breast cancer genomes

Background

DNA palindromes are a unique pattern of repeat sequences that are present in the human genome. It consists of a sequence of nucleotides in which the second half is the complement of the first half but appearing in reverse order. These palindromic sequences may have a significant role in DNA replication, transcription and gene regulation processes. They occur frequently in human cancers by clustering at specific locations of the genome that undergo gene amplification and tumorigenesis. Moreover, some studies showed that palindromes are clustered in amplified regions of breast cancer genomes especially in chromosomes (chr) 8 and 11. With the large number of personal genomes and cancer genomes becoming available, it is now possible to study their association to diseases using computational methods. Here, we conducted a pilot study on chromosomes 8 and 11 of cancer genomes to identify computationally the differentially occurring palindromes.

Methods

We processed 69 breast cancer genomes from The Cancer Genome Atlas including serum-normal and tumor genomes, and 1000 Genomes to serve as control group. The Biological Language Modelling Toolkit (BLMT) computes palindromes in whole genomes. We developed a computational pipeline integrating BLMT to compute and compare prevalence of palindromes in personal genomes.

Results

We carried out a pilot study on chr 8 and chr 11 taking into account single nucleotide polymorphisms, insertions and deletions. Of all the palindromes that showed any variation in cancer genomes, 38% of what were near breast cancer genes happened to be the most differentiated palindromes in tumor (i.e. they ranked among the top 25% by our heuristic measure).

Conclusions

These results will shed light on the prevalence of palindromes in oncogenes and the mutations that are present in the palindromic regions that could contribute to genomic rearrangements, and breast cancer progression.

Clustering DNA sequences using the out-of-place measure with reduced n-grams

The alignment-free n-gram based method with the out-of-place measures as the distance has been successfully applied to automatic text or natural languages categorization in real time. However, it is not clear about its performance and the selection of n for comparing genome sequences. Here we propose a symmetric version of the out-of-place measure and a new approach for finding the optimal range of n to construct a phylogenetic tree with the symmetric out-of-place measures. Our method is then applied to real genome sequence datasets. The resulting phylogenetic trees are matching with the standard biological classification. It shows that our proposed method is a very powerful tool for phylogenetic analysis in terms of both classification accuracy and computation efficiency.