Annangarachari Krishnamachari

Discovery of novel tumor suppressor p53 response elements using information theory

An accurate method for locating genes under tumor suppressor p53 control that is based on a well-established mathematical theory and built using naturally occurring, experimentally proven p53 sites is essential in understanding the complete p53 network. We used a molecular information theory approach to create a flexible model for p53 binding. By searching around transcription start sites in human chromosomes 1 and 2, we predicted 16 novel p53 binding sites and experimentally demonstrated that 15 of the 16 (94%) sites were bound by p53. Some were also bound by the related proteins p63 and p73. Thirteen of the adjacent genes were controlled by at least one of the proteins. Eleven of the 16 sites (69%) had not been identified previously. This molecular information theory approach can be extended to any genetic system to predict new sites for DNA-binding proteins.

Sequence Variability and Long-Range Dependence in DNA: An Information Theoretic Perspective

Investigation of the symbolic DNA sequence in terms of its structure and organization is a challenging problem. Inherent uncertainty and sequence variability makes information theoretic framework eminently suitable for applying to variety of computational biology problems. This paper highlights the properties of parametric and non-parametric entropy measures and focusses on few applications where entropic measures have been used. The link between Tsallis entropy and power-law is drawn using maximum entropy principle in capturing the well-known long-range dependence in DNA sequences.

On the origin of three base periodicity in genomes.

Genomes of almost all organisms have been found to exhibit several periodicities, the most prominent one is the three base periodicity. It is more pronounced in the gene coding regions and has been exploited to identify the segments of a genome that code for a protein. The reason for this three base periodicity in the gene-coding region has been attributed to inhomogeneous nucleotide compositions in the three codon positions. However, this reason cannot explain the three base periodicity present at the level of the whole genome where the codon concept is not applicable. Even though the distribution of each nucleotide is uniform at the positions 0(mod 3), 1(mod 3) and 2(mod 3) when the whole genome data is considered, our analysis reveals that the three base periodicity is arising because of higher correlations among the nucleotides separated by three bases.

Nucleotide correlation based measure for identifying origin of replication in genomic sequences

Computational prediction of the origin of replication is a challenging problem and of immense interest to biologists. Several methods have been proposed for identifying the replicon site for various classes of organisms. However, these methods have limited applicability since the replication mechanism is different in different organisms. We propose a correlation measure and show that it is correctly able to predict the origin of replication in most of the bacterial genomes. When applied to Methanocaldococcus jannaschii, Plasmodium falciparum apicoplast and Nicotiana tabacum plastid, this correlation based method is able to correctly predict the origin of replication whereas the generally used GC skew measure fails. Thus, this correlation based measure is a novel and promising tool for predicting the origin of replication in a wide class of organisms. This could have important implications in not only gaining a deeper understanding of the replication machinery in higher organisms, but also for drug discovery.

Identification and characterization of ARS‐like sequences as putative origin(s) of replication in human malaria parasite Plasmodium falciparum

DNA replication is a fundamental process in genome maintenance, and initiates from several genomic sites (origins) in eukaryotes. In Saccharomyces cerevisiae, conserved sequences known as autonomously replicating sequences (ARSs) provide a landing pad for the origin recognition complex (ORC), leading to replication initiation. Although origins from higher eukaryotes share some common sequence features, the definitive genomic organization of these sites remains elusive. The human malaria parasite Plasmodium falciparum undergoes multiple rounds of DNA replication; therefore, control of initiation events is crucial to ensure proper replication. However, the sites of DNA replication initiation and the mechanism by which replication is initiated are poorly understood. Here, we have identified and characterized putative origins in P. falciparum by bioinformatics analyses and experimental approaches. An autocorrelation measure method was initially used to search for regions with marked fluctuation (dips) in the chromosome, which we hypothesized might contain potential origins. Indeed, S. cerevisiae ARS consensus sequences were found in dip regions. Several of these P. falciparum sequences were validated with chromatin immunoprecipitation‐quantitative PCR, nascent strand abundance and a plasmid stability assay. Subsequently, the same sequences were used in yeast to confirm their potential as origins in vivo. Our results identify the presence of functional ARSs in P. falciparum and provide meaningful insights into replication origins in these deadly parasites. These data could be useful in designing transgenic vectors with improved stability for transfection in P. falciparum.

Prediction of replication sites in Saccharomyces cerevisiae genome using DNA segment properties: Multi-view ensemble learning (MEL) approach

Autonomous replication sequences (ARS) are essential for the replication of Saccharomyces cerevisiae genome. The content and context of ARS sites are distinct from other segments of the genome and these factors influence the conformation and thermodynamic profile of DNA that favor binding of the origin recognition complex proteins. Identification of ARS sites in the genome is a challenging task because of their organizational complexity and degeneracy present across the intergenic regions. We considered a few properties of DNA segments and divided them into multiple subsets (views) for computational prediction of ARS sequences. Our approach utilized these views for learning classification models in an ensemble manner and accordingly predictions were made. This approach maximized the prediction accuracy over the traditional way where all features are selected at once. Our study also revealed that major groove width and major groove depth are the most prominent properties that distinguished ARS from other segments of the genome. Our investigation also provides clue about the most suitable classifier for a given feature set, and this strategy may be useful for finding ARS in other closely related species.

Context based computational analysis and characterization of ARS consensus sequences (ACS) of Saccharomyces cerevisiae genome.

Genome-wide experimental studies in Saccharomyces cerevisiae reveal that autonomous replicating sequence (ARS) requires an essential consensus sequence (ACS) for replication activity. Computational studies identified thousands of ACS like patterns in the genome. However, only a few hundreds of these sites act as replicating sites and the rest are considered as dormant or evolving sites. In a bid to understand the sequence makeup of replication sites, a content and context-based analysis was performed on a set of replicating ACS sequences that binds to origin-recognition complex (ORC) denoted as ORC-ACS and non-replicating ACS sequences (nrACS), that are not bound by ORC. In this study, DNA properties such as base composition, correlation, sequence dependent thermodynamic and DNA structural profiles, and their positions have been considered for characterizing ORC-ACS and nrACS. Analysis reveals that ORC-ACS depict marked differences in nucleotide composition and context features in its vicinity compared to nrACS. Interestingly, an A-rich motif was also discovered in ORC-ACS sequences within its nucleosome-free region. Profound changes in the conformational features, such as DNA helical twist, inclination angle and stacking energy between ORC-ACS and nrACS were observed. Distribution of ACS motifs in the non-coding segments points to the locations of ORC-ACS which are found far away from the adjacent gene start position compared to nrACS thereby enabling an accessible environment for ORC-proteins. Our attempt is novel in considering the contextual view of ACS and its flanking region along with nucleosome positioning in the S. cerevisiae genome and may be useful for any computational prediction scheme.

Ensemble of deep long short term memory networks for labelling origin of replication sequences

Advancement in sequence data generation technologies are churning out voluminous omics data and posing a massive challenge to annotate the biological functional features. Sequence data from the well studied model organism Saccharomyces cerevisiae has been commonly used to test and validate in silico prediction methods. DNA replication is a critical step in the cellular process and the sequence location where this process originates in the genomic landscape is generally referred as origin of replication. In this paper we investigate the application bidirectional Long Short Term (LSTM) Networks to predict origin of replication sequences. Long Short Term Memory (LSTM) networks have recently been shown to yield state of the art performance in speech recognition, and music generation. These networks are capable of learning long term patterns via the use of multiplication gates. This paper utilizes Deep bidirectional LSTM for prediction of origin of replication sequences belonging to the organism Saccharomyces cerevisiae. Results demonstrate that LSTMs outperform the commonly used machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Hidden Markov Model (HMM). An important additional advantage of LSTMs is that they work directly on the sequences and obviate the need for hand coded features.

Computational prediction of origin of replication in bacterial genomes using correlated entropy measure (CEM).

We have carried out an analysis on 500 bacterial genomes and found that the de-facto GC skew method could predict the replication origin site only for 376 genomes. We also found that the auto-correlation and cross-correlation based methods have a similar prediction performance. In this paper, we propose a new measure called correlated entropy measure (CEM) which is able to predict the replication origin of all these 500 bacterial genomes. The proposed measure is context sensitive and thus a promising tool to identify functional sites. The process of identifying replication origins from the output of CEM and other methods has been automated to analyze a large number of genomes in a faster manner. We have also explored the applicability of SVM based classification of the workability of each of these methods on all the 500 bacterial genomes based on its length and GC content.