Notable clustering of transcription-factor-binding motifs in human pericentric regions and its biological significance

Abstract

Since oligonucleotide composition in the genome sequence varies significantly among species even among those possessing the same genome G + C%, the composition has been used to distinguish a wide range of genomes and called as “genome signature”. Oligonucleotides often represent motif sequences responsible for sequence-specific protein binding (e.g., transcription-factor binding). Occurrences of such motif oligonucleotides in the genome should be biased compared to those observed in random sequences and may differ among genomes and genomic portions. Self-Organizing Map (SOM) is a powerful tool for clustering high-dimensional data such as oligonucleotide composition on one plane. We previously modified the conventional SOM for genome informatics to batch learning SOM or “BLSOM”. When we constructed BLSOMs to analyze pentanucleotide composition in 20-, 50-, and 100-kb sequences derived from the human genome, BLSOMs did not classify human sequences according to chromosome but revealed several specific zones composed primarily of sequences derived from pericentric regions. Interestingly, various transcription-factor-binding motifs were characteristically overrepresented in pericentric regions but underrepresented in most genomic sequences. When we focused on much shorter sequences (e.g., 1 kb), the clustering of transcription-factor-binding motifs was evident in pericentric, subtelomeric and sex chromosome pseudoautosomal regions. The biological significance of the clustering in these regions was discussed in connection with cell-type and -stage-dependent chromocenter formation and nuclear organization.Electronic supplementary materialThe online version of this article (doi:10.1007/s10577-013-9371-y) contains supplementary material, which is available to authorized users.

CG-containing oligonucleotides and transcription factor-binding motifs are enriched in human pericentric regions

“…Oligonucleotide composition can also specify both inter-and intragenomic differences (Abe et al, 2006a;Iwasaki et al, 2013). The present study analyzes intragenomic, global characteristics of oligonucleotide composition in the human genome at the level of tens of kb or much longer.…”

An artificial intelligence approach fit for tRNA gene studies in the era of big sequence data

“…Since metagenomic sequences have attracted broad scientific, industrial and medical interest, tRNADB-CE has included tDNAs obtained from The distances of vectorial data between neighboring lattice points on PartsTetra are visualized as grayness levels with a U-matrix method as described by Iwasaki et al (2013). metagenomic sequences (abbreviated to metagenomic tDNAs); these metagenomic tDNAs are analyzed here with BLSOM. Since metagenomic sequences are probably derived not only from bacteria but also archaea and fungi, we have constructed PartsTetra with speciesunknown metagenomic tDNAs plus species-known bacterial, archaeal and fungal tDNAs, comprising 0.6 million tDNAs in total (Both in Fig.…”

“…ie.niigata-u.ac.jp/?BLSOM). Distances of weight vectors between neighboring lattice points on BLSOM can be visualized as black levels with a U-matrix method (Ultsch, 1993), and this provides information about similarity of oligonucleotide composition in local areas on BLSOM (Iwasaki et al, 2013).…”

“…An analysis of the human genome with pentanucleotide BLSOM has unexpectedly found evident enrichment of many kinds of transcription factor-binding motifs in pericentric heterochromatin regions (Iwasaki et al, 2013), showing that BLSOM effectively detects characteristic, combinatorial occurrences of functional-motif oligonucleotides in genomic sequences with no prior knowledge.…”

Self-Organizing Map for Characterizing Heterogeneous Nucleotide and Amino Acid Sequence Motifs

“…The simplest ones use word frequencies (e.g., dinucletide, trinucleotide, or longer oligonucleotide frequencies, [35,36,42]). Nucleotide motifs can also be codified into a 3D entity and Euclidian distance can be computed between two such recoded motifs [43,44].…”