Additive methods for genomic signatures

Abstract

BackgroundStudies exploring the potential of Chaos Game Representations (CGR) of genomic sequences to act as “genomic signatures” (to be species- and genome-specific) showed that CGR patterns of nuclear and organellar DNA sequences of the same organism can be very different. While the hypothesis that CGRs of mitochondrial DNA sequences can act as genomic signatures was validated for a snapshot of all sequenced mitochondrial genomes available in the NCBI GenBank sequence database, to our knowledge no such extensive analysis of CGRs of nuclear DNA sequences exists to date.ResultsWe analyzed an extensive dataset, totalling 1.45 gigabase pairs, of nuclear/nucleoid genomic sequences (nDNA) from 42 different organisms, spanning all major kingdoms of life. Our computational experiments indicate that CGR signatures of nDNA of two different origins cannot always be differentiated, especially if they originate from closely-related species such as H. sapiens and P. troglodytes or E. coli and E. fergusonii. To address this issue, we propose the general concept of additive DNA signature of a set (collection) of DNA sequences. One particular instance, the composite DNA signature, combines information from nDNA fragments and organellar (mitochondrial, chloroplast, or plasmid) genomes. We demonstrate that, in this dataset, composite DNA signatures originating from two different organisms can be differentiated in all cases, including those where the use of CGR signatures of nDNA failed or was inconclusive. Another instance, the assembled DNA signature, combines information from many short DNA subfragments (e.g., 100 basepairs) of a given DNA fragment, to produce its signature. We show that an assembled DNA signature has the same distinguishing power as a conventionally computed CGR signature, while using shorter contiguous sequences and potentially less sequence information.ConclusionsOur results suggest that, while CGR signatures of nDNA cannot always play the role of genomic signatures, composite and assembled DNA signatures (separately or in combination) could potentially be used instead. Such additive signatures could be used, e.g., with raw unassembled next-generation sequencing (NGS) read data, when high-quality sequencing data is not available, or to complement information obtained by other methods of species identification or classification.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1157-8) contains supplementary material, which is available to authorized users.

Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

“…More importantly, in two independent geo gastric cancer datasets within the STAD, the prognostic power of the 4-gene signature was verified. Gene signature is often applied to forcast the prognosis of a variety of tumors in the past few years [22], which is even better than TNM staging and histopathological diagnosis in some extent [23]. Gene signatures based on ATGs have been reported in a variety of cancers, such as serous ovarian cancer, breast cancer, colon cancer and glioma [24][25][26][27].…”

Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

“…More importantly, in two independent geo gastric cancer datasets within the STAD, the prognostic power of the 4-gene signature was veri ed. Gene signature is often applied to forcast the prognosis of a variety of tumors in the past few years [22], which is even better than TNM staging and histopathological diagnosis in some extent [23]. Gene signatures based on ATGs have been reported in a variety of cancers, such as serous ovarian cancer, breast cancer, colon cancer and glioma [24][25][26][27].…”

Formalin-fixed paraffin-embedded sample conditions for deep next generation sequencing

“…The third limitation is the computational power required. 31, 32 Future whole-genome sequences will need to be analyzed when NGS analysis is expanded into non-coding regions of the genome, and this will require “super computers”. Currently, these are the barriers, but it is also expected that these issues may be resolved with the advancement and widespread use of the technology, just like personal computers have become cheaper and faster.…”