Biology subjects

Rahman, M. S.

Publications and source records attributed to Rahman, M. S..

At least 73 records · Page 4Linked to original sources

bioRxiv2020

Microbiome Dynamics of Bovine Mastitis Progression and Genomic Determinants

The milk of lactating cows presents a complex ecosystem of interconnected microbial communities which can impose a significant influence on the pathophysiology of mastitis. Previously, we reported the alteration of microbiome (bacteria, archaea, virus) composition between clinical mastitis (CM) and healthy (H) milk. We hypothesized possible dynamic shifts of microbiome compositions with the progress of different pathological states of mastitis (CM, Recurrent CM; RCM, Subclinical Mastitis; SCM) determined by its favoring genomic potentials. To evaluate this hypothesis, we employed whole metagenome sequencing (WMS) in 20 milk samples (CM = 5, RCM = 6, SCM = 4, H = 5) to unravel the microbiome dynamics, interrelation, and relevant metabolic functions. PathoScope (PS) and MG-RAST (MR) analyses mapped the WMS data to 442 bacterial, 58 archaeal and 48 viral genomes with distinct variation in microbiome composition and abundances across these metagenomes (CM>H>RCM>SCM). PS analysis identified 385, 65, 80 and 144 bacterial strains in CM, RCM, SCM, and H milk, respectively, with an inclusion of 67.19% previously unreported opportunistic strains in mastitis metagenomes. Moreover, MR detected 56, 13, 9 and 46 archaeal, and 40, 24, 11 and 37 viral genera in CM, RCM, SCM and H-milk metagenomes, respectively. The CM-microbiomes had closest association with RCM-microbiomes followed by SCM, and H-microbiomes. Furthermore, we identified 333, 304, 183 and 50 virulence factors-associated genes (VFGs), and 48, 31, 11 and 6 antibiotic resistance genes (AGRs) in CM, RCM, SCM, and H-microbiomes, respectively, showing a significant correlation between the relative abundances of VFGs (p = 0.001), ARGs (p = 0.0001), and associated bacterial taxa. We also detected correlated variations in the presence and abundance of several metabolic functional genes related to bacterial colonization, proliferation, chemotaxis, motility and invasion, oxidative stress, virulence and pathogenicity, phage integration and excision, biofilm-formation, and quorum-sensing to be associated with different episodes of mastitis. Therefore, profiling the dynamics of microbiome in different states of mastitis, concurrent VFGs, ARGs, and genomic functional correlations will contribute to developing microbiome-based diagnostics and therapeutics for bovine mastitis, and carries significant implications on curtailing the economic fallout from this disease.

microbiology↗

bioRxiv2020

Comprehensive annotations of the mutational spectra of SARS-CoV-2 spike protein: a fast and accurate pipeline

In order to explore nonsynonymous mutations and deletions in the spike (S) protein of SARS-CoV-2, we comprehensively analyzed 35,750 complete S protein gene sequences from across six continents and five climate zones around the world, as documented in the GISAID database as of June 24th, 2020. Through a custom Python-based pipeline for analyzing mutations, we identified 27,801 (77.77 % of spike sequences) mutated strains compared to Wuhan-Hu-1 strain. 84.40% of these strains had only single amino-acid (aa) substitution mutations, but an outlier strain from Bosnia and Herzegovina (EPI_ISL_463893) was found to possess six aa substitutions. The D614G variant of the major G clade was found to be predominant across circulating strains in all climates. We also identified 988 unique aa substitution mutations distributed across 660 positions within the spike protein, with eleven sites showing high variability - these sites had four types of aa variations at each position. Besides, 17 in-frame deletions at four major regions (three in N-terminal domain and one just downstream of the RBD) may have possible impact on attenuation. Moreover, the mutational frequency differed significantly (p= 0.003, Kruskal-Wallis test) among the SARS-CoV-2 strains worldwide. This study presents a fast and accurate pipeline for identifying nonsynonymous mutations and deletions from large dataset for any particular protein coding sequence and presents this S protein data as representative analysis. By using separate multi-sequence alignment with MAFFT, removing ambiguous sequences and in-frame stop codons, and utilizing pairwise alignment, this method can derive nonsynonymus mutations (Reference:Position:Strain). We believe this will aid in the surveillance of any proteins encoded by SARS-CoV-2, and will prove to be crucial in tracking the ever-increasing variation of many other divergent RNA viruses in the future.

evolutionary biology↗

bioRxiv2020

Analyzing hCov genome sequences: Applying Machine Intelligence and beyond

BackgroundCovid-19 pandemic, caused by the SARS-CoV-2 genome sequence of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analyzed. MethodsWe present here an analysis pipeline comprising a classification exercise to identify the virulence of the genome sequences and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. ResultsWe have classified the SARS-CoV-2 genome sequences with high accuracy and predicted the mutations in the sites of Interest. ConclusionsIn a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.

bioinformatics↗

bioRxiv2020

Cat fleas in flux: Rampant gene duplication, genome size plasticity, and paradoxical Wolbachia infection

BackgroundFleas (Insecta: Siphonaptera) are small flightless parasites of birds and mammals; their blood-feeding can transmit many serious pathogens (i.e. the etiological agents of bubonic plague, endemic and murine typhus). The lack of flea genome assemblies has hindered research, especially comparisons to other disease vectors. Accordingly, we sequenced the genome of the cat flea, Ctenocephalides felis, an insect with substantial human health and veterinary importance across the globe. ResultsBy combining Illumina and PacBio sequencing with Hi-C scaffolding techniques, we generated a chromosome-level genome assembly for C. felis. Unexpectedly, our assembly revealed extensive gene duplication across the entire genome, exemplified by [~]38% of protein-coding genes with two or more copies and over 4,000 tRNA genes. A broad range of genome size determinations (433-551 Mb) for individual fleas sampled across different populations supports the widespread presence of fluctuating copy number variation (CNV) in C. felis. Similarly broad genome sizes were also calculated for individuals of Xenopsylla cheopis (Oriental rat flea), indicating that this remarkable "genome-in-flux" phenomenon could be a siphonapteran-wide trait. Finally, from the C. felis sequence reads we also generated closed genomes for two novel strains of Wolbachia, one parasitic and one symbiotic, found to co-infect individual fleas. ConclusionRampant CNV in C. felis has dire implications for gene-targeting pest control measures and stands to complicate standard normalization procedures utilized in comparative transcriptomics analysis. Coupled with co-infection by novel Wolbachia endosymbionts - potential tools for blocking pathogen transmission - these oddities highlight a unique and underappreciated disease vector.

genomics↗

bioRxiv2020

Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2 etiologic agent of global pandemic COVID-19: an in silico approach

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing pandemic of coronavirus disease 2019 (COVID-19), a public health emergency of international concern declared by the World Health Organization (WHO). An immuno-informatics approach along with comparative genomic was applied to design a multi-epitope-based peptide vaccine against SARS-CoV-2 combining the antigenic epitopes of the S, M and E proteins. The tertiary structure was predicted, refined and validated using advanced bioinformatics tools. The candidate vaccine showed an average of [≥] 90.0% world population coverage for different ethnic groups. Molecular docking of the chimeric vaccine peptide with the immune receptors (TLR3 and TLR4) predicted efficient binding. Immune simulation predicted significant primary immune response with increased IgM and secondary immune response with high levels of both IgG1 and IgG2. It also increased the proliferation of T-helper cells and cytotoxic T-cells along with the increased INF-{gamma} and IL-2 cytokines. The codon optimization and mRNA secondary structure prediction revealed the chimera is suitable for high-level expression and cloning. Overall, the constructed recombinant chimeric vaccine candidate demonstrated significant potential and can be considered for clinical validation to fight against this global threat, COVID-19.

microbiology↗

bioRxiv2019

Computations, optimization and tuning of deep feedforward neural networks

This article presents an overview of the generalized formulations of the computations, optimization, and tuning of a deep feedforward neural network. A small network has been used to systematically explain the computing steps, which were then used to establish the generalized forms of the computations in forward and backward propagations for larger networks. Additionally, some of the commonly used cost functions, activation functions, optimization algorithms, and hyper-parameters tuning approaches have been discussed.

neuroscience↗

bioRxiv2019

Principles of tactile search over the body

Although we experience complex patterns over our entire body, how we selectively perceive multi-site touch over our bodies remains poorly understood. Here, we characterized tactile search behavior over the body using a tactile analog of the classic visual search task. Participants judged whether a target stimulus (e.g., 10-Hz vibration) was present or absent on the upper or lower limbs. When present, the target stimulus could occur alone or with distractor stimuli (e.g., 30-Hz vibrations) on other body locations. We varied the number and spatial configurations of the distractors as well as the target and distractor frequencies and measured the impact of these factors on search response times. First, we found that response times were faster on target-present trials compared to target-absent trials. Second, response times increased with the number of stimulated sites, suggesting a serial search process. Third, search performance differed depending on stimulus frequencies. This frequency-dependent behavior may be related to perceptual grouping effects based on timing cues. We constructed models to explore how the locations of the tactile cues influenced search behavior. Our modeling results reveal that, in isolation, cues on the index fingers make relatively greater contributions to search performance compared to stimulation experienced on other body sites. Additionally, co-stimulation of sites within the same limb or simply on the same body side preferentially influence search behavior. Our collective findings identify some principles of attentional search that are common to vision and touch, but others that highlight key differences that may be unique to body-based spatial perception. New & NoteworthyLittle is known about how we selectively experience multi-site touch over the body. Using a tactile analog of the classic visual search paradigm, we show that tactile search behavior for flutter cues is generally consistent with a serial search process. Modeling results reveal the preferential contributions of index finger stimulation and two-site interactions involving ipsilateral and within-limb patterns. Our results offer initial evidence for spatial and temporal principles underlying tactile search behavior over the body.

neuroscience↗

bioRxiv2019

SAINT: Self-Attention AugmentedInception-Inside-Inception Network Improves ProteinSecondary Structure Prediction

MotivationProtein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g., X-ray crystallography, nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the secondary structure of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of secondary structure contains more useful information and is much more challenging than the Q3 prediction. ResultsWe present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception (Deep3I) network in order to effectively capture both the short-range and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step towards the accurate and reliable prediction of secondary structures of proteins. AvailabilitySAINT is freely available as an open source project at https://github.com/SAINTProtein/SAINT.

bioinformatics↗

bioRxiv2019

Insights into protein structural, physicochemical, and functional consequences of missense variants in 1,330 disease-associated human genes

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variants on protein structure and function being especially challenging. Here we evaluated the burden of amino acids affected in pathogenic variants (n=32,923) compared to the variants (n=164,915) from the general population in 1,330 disease-associated genes on forty protein features using over 14,000 experimentally-solved 3D structures. By analyzing the whole gene/variant set jointly, we identified 18 features associated with 3D mutational hotspots that are generally important for protein fitness and stability. Individual analyses performed for twenty-four protein functional classes further revealed 240 characteristics of mutational hotspots in total, including new associations recapitulating the sheer diversity across proteins essential structural regions. We demonstrated that the function-specific features of variants correspond to the readouts of mutagenesis experiments and positively correlate with clinically-interpreted pathogenic and benign missense variants. Finally, we made our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step towards translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the pathogenicity of variants in terms of the perturbed molecular mechanisms.

genetics↗

bioRxiv2019

Prefix Block-Interchanges on Binary and Ternary Strings

The genome rearrangement problem computes the minimum number of operations that are required to sort all elements of a permutation. A block-interchange operation exchanges two blocks of a permutation which are not necessarily adjacent and in a prefix block-interchange, one block is always the prefix of that permutation. In this paper, we focus on applying prefix block-interchanges on binary and ternary strings. We present upper bounds to group and sort a given binary/ternary string. We also provide upper bounds for a different version of the block-interchange operation which we refer to as the restricted prefix block-interchange. We observe that our obtained upper bound for restricted prefix block-interchange operations on binary strings is better than that of other genome rearrangement operations to group fully normalized binary strings. Consequently, we provide a linear-time algorithm to solve the problem of grouping binary normalized strings by restricted prefix block-interchanges. We also provide a polynomial time algorithm to group normalized ternary strings by prefix block-interchange operations. Finally, we provide a classification for ternary strings based on the required number of prefix block-interchange operations.

bioinformatics↗

bioRxiv2019

CRISPRpred(SEQ): a sequence based tool for sgRNA on target activity prediction

An accurate and fast genome editing tool can be used to treat genetic diseases, modify crops genetically etc. However, a tool that has low accuracy can be risky to use, as incorrect genome editing may have severe consequences. Although many tools have been developed in the past, there are still room for further improvement. In this paper, we present CRISPRpred(SEQ), a sequence based tool for sgRNA on target activity prediction that leverages only traditional machine learning techniques. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. In spite of using only traditional machine learning methods, we are able to beat DeepCRISPR for the three out of four cell lines in the benchmark dataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines), which is quite outstanding.

bioinformatics↗

bioRxiv2019

Multimodal frequency representations are embedded in modality-defined cortical sensory systems

Sensory information is represented and elaborated in hierarchical cortical systems that are thought to be dedicated to individual sensory modalities. This traditional view of sensory cortex organization has been challenged by recent evidence of multimodal responses in primary and association sensory areas. Although it is indisputable that sensory areas respond to multiple modalities, it remains unclear whether these multimodal responses reflect selective information processing for particular stimulus features. Here, we used fMRI adaptation to identify brain regions that are sensitive to the temporal frequency information contained in auditory, tactile, and audiotactile stimulus sequences. A number of brain regions distributed over the parietal and temporal lobes exhibited frequency-selective temporal response modulation for both auditory and tactile stimulus events, as indexed by repetition suppression effects. A smaller set of regions responded to crossmodal adaptation sequences in a frequency-dependent manner. Despite an extensive overlap of multimodal frequency-selective responses across the parietal and temporal lobes, representational similarity analysis revealed a cortical \"regional landscape\" that clearly reflected distinct somatosensory and auditory processing systems that converged on modality-invariant areas. These structured relationships between brain regions were also evident in spontaneous signal fluctuation patterns measured at rest. Our results reveal that multimodal processing in human cortex can be feature-specific and that multimodal frequency representations are embedded in the intrinsically hierarchical organization of cortical sensory systems.\n\nSignificance StatementA hallmark of traditional brain organization models is the segregation of signals from the different senses in modality-dedicated brain regions. Recent evidence showing multimodal activity in brain regions thought to be dedicated to a single modality have challenged the traditional sensory cortex model. Notably, few studies have explored the feature-specificity of multimodal responses found in sensory cortex. Here, we used fMRI adaptation to identify parietal and temporal cortex regions which exhibited sensitivity to both tactile and auditory frequency information. These univariate results demonstrate that multimodal processing in sensory cortex can be feature-specific. Using the same data, though, we found clear evidence of modality-based cortical organization estimated from multivariate response patterns and spontaneous BOLD signal fluctuations. Thus, our results reveal an embedding of feature-specific multimodal processing in traditionally-defined cortical systems.

neuroscience↗

bioRxiv2019

A Comprehensive Molecular Portrait of Human Urine-derived Renal Progenitor Cells

BackgroundHuman urine is now recognised as a non-invasive source of stem cells with regeneration potential. These cells are mesenchymal stem cells but their detailed molecular and cellular identities are poorly defined. Furthermore, unlike the mouse, the gene regulatory network driving self-renewal and differentiation into functional renal cells in vitro remain unresolved.\n\nMethodsWe isolated urine stem cells from 10 individuals from both genders and distinct ages, characterized them as renal progenitor cells and explored the gene regulatory network sustaining self-renewal.\n\nResultsThese cells express pluripotency-associated proteins-TRA-1-60, TRA-1-81, SSEA4, C-KIT and CD133. Expression of pluripotency-associated proteins enabled rapid reprogramming into iPSCs using episomal-based plasmids without pathway perturbations. Transcriptome analysis revealed expression of a plethora of nephrogenesis-related genes such as SIX2, OSR1, CITED1, NPHS2, NPHS1, PAX2, SALL1, AQP2, EYA1, SLC12A1 and UMOD. As expected, the cells transport Albumin by endocytosis. Based on this, we refer to these cells as urine derived renal progenitor cells-UdRPCs. Associated GO-term analysis of UdRPCs and UdRPC-iPSCs underlined their renal identity and functionality. Upon differentiation by WNT activation using the GSK3{beta}-inhibitor (CHIR99021), transcriptome and KEGG pathway analysis revealed upregulation of WNT-associated genes-AXIN2, JUN and NKD1. Protein interaction network identified JUN- a downstream target of the WNT pathway in association with STAT3, ATF2 and MAPK1 as a putative regulator of self-renewal and differentiation in UdRPCs. Furthermore, like pluripotent stem cells, self-renewal is maintained by FGF2-driven TGF{beta}-SMAD2/3 pathway.\n\nConclusionThis in vitro model and the data presented should lay the foundation for studying nephrogenesis in man.\n\nSignificance StatementHuman urine is a non-invasive source of stem cells with regeneration potential. Here, we investigated the cellular and molecular identities, and the gene regulation driving self-renewal and differentiation of these cells in vitro. These cells express pluripotency-associated markers enabling easy reprogramming. Based on the expression of renal associated genes, proteins and functionality, we refer to these cells as urine derived renal progenitor cells-UdRPCs. CHIR99021-induced differentiation of UdRPCs activated WNT-related genes-AXIN2, JUN and NKD1. Protein interaction network identified JUN as a putative regulator of differentiation whereas self-renewal is maintained by FGF2-driven TGF{beta}-SMAD2/3. Our data will enhance understanding of the molecular identities of UdRPCs, and enable the generation of renal disease models in vitro and eventually kidney-associated regenerative therapies.

developmental biology↗

bioRxiv2019

SICaRiO: Short Indel Call filteRing with bOosting

Despite impressive improvement in the next-generation sequencing technology, reliable detection of indels is still a difficult endeavour. Recognition of true indels is of prime importance in many applications, such as, personalized health care, disease genomics, population genetics etc. Recently, advanced machine learning techniques have been successfully applied to classification problems with large-scale data. In this paper, we present SICaRiO, a gradient boosting classifier for reliable detection of true indels, trained with gold-standard dataset from genome-in-a-bottle (GIAB) consortium. Our filtering scheme significantly improves the performance of each variant calling pipeline used in GIAB and beyond. SICaRiO uses genomic features which can be computed from publicly available resources, hence, we can apply it on any indel callsets not having sequencing pipeline-specific information (e.g., read depth). This study also sheds lights on prior genomic contexts responsible for indel calling error made by sequencing platforms. We have compared prediction difficulty for three indel categories over different sequencing pipelines. We have also ranked genomic features according to their predictivity in determining false indel calls.

bioinformatics↗

bioRxiv2019

CHilEnPred: CNN Model With Hilbert Curve Representation of DNA Sequence For Enhancer Prediction

MotivationEnhancers are distal cis-acting regulating regions that play a vital role in gene transcription. However, due to the inherent nature of enhancers being linearly distant from the affected gene in an irregular manner while being spatially close at the same time, systematically predicting enhancers has been a challenging task. Although several computational predictor models through both epigenetic marker analysis and sequence-based analysis have been proposed, they lack generalization capacity across different enhancer datasets and have feature dependency. On the other hand, the recent proliferation of deep learning methods has opened previously unknown avenues of approach for sequence analysis tasks which eliminates feature dependency and achieves greater generalization. Therefore, harnessing the power of deep learning based sequence analysis techniques to develop a more generalized model than the ones developed before to predict enhancer region in a DNA sequence is a topic of interest in bioinformatics. ResultsIn this study, we develop the predictor model CHilEnPred that has been trained with the visual representation of the DNA sequences with Hilbert Curve. We report our computational prediction result on FANTOM5 dataset where CHilEnPred achieves an accuracy of 94.97% and AUC of 0.987 on test data. AvailabilityOur CHilEnPred model can be freely accessed at https://github.com/iatahmid/chilenpred Contactmsrahman@cse.buet.ac.bd

bioinformatics↗

bioRxiv2019

NORTH: a highly accurate and scalable Naive Bayes based ORTHologous gene clustering algorithm

BackgroundThe principal objective of comparative genomics is inferring attributes of an unknown gene by comparing it with well-studied genes. In this regard, identifying orthologous genes plays a pivotal role as the orthologous genes remain less diverged in the course of evolution. However, identifying orthologous genes is often difficult, slow, and idiosyncratic, especially in the presence of multiplicity of domains in proteins, evolutionary dynamics (gene duplication, transfer, loss, introgression etc.), multiple paralogous genes, incomplete genome data, and for distantly related species where similarity is hard to recognize. MotivationAdvances in identifying orthologs have mostly been constrained to developing databases of genes or methods which involve computationally expensive BLAST search or constructing phylogenetic trees to infer orthologous relationships. These methods do not generally scale well and cannot analyze large amount of data from diverse organisms with high accuracy. Moreover, most of these methods involve manual parameter tuning, and hence are neither fully automated nor free from human bias. ResultsWe present NORTH, a novel, automated, highly accurate and scalable machine learning based orhtologous gene clustering method. We have utilized the biological basis and intuition of orthologous genes and made an effort to incorporate appropriate ideas from machine learning (ML) and natural language processing (NLP). We have discovered that the BLAST search based protocols deeply resemble a "text classification" problem. Thus, we employ the robust bag-of-words model accompanied by a Naive Bayes classifier to cluster the orthologous genes. We studied 1,255,877 genes in the largest 250 ortholog clusters from the KEGG database, across 3,880 organisms comprising the six major groups of life, namely, Archaea, Bacteria, Animals, Fungi, Plants and Protists. Despite having more than a million of genes on distantly related species with acute data imbalance, NORTH is able to cluster them with 98.48% Precision, 98.43% Recall and 98.44% F1 score, showing that automatic orthologous gene clustering can be both highly accurate and scalable. NORTH is available as a web interface with a server side application, along with cross-platform native applications (available at https://nibtehaz.github.io/NORTH/) - allowing queries based on individual genes.

bioinformatics↗

bioRxiv2018

Trends and projections of universal health coverage indicators in Ghana, 1995-2030: A national and subnational study

Ghana has made significant stride towards universal health coverage (UHC) by implementing the National Health Insurance Scheme (NHIS) in 2003. This paper investigates the progress of UHC indicators in Ghana from 1995 to 2030 and makes future predictions up to 2030 to assess the probability of achieving UHC targets. National representative surveys of Ghana were used to assess health service coverage and financial risk protection. The analysis estimated the coverage of 13 prevention and four treatment service indicators at the national level and across wealth quintiles. In addition, this analysis calculated catastrophic health payments and impoverishment to assess financial hardship and used a Bayesian regression model to estimate trends and future projections as well as the probabilities of achieving UHC targets by 2030. Wealth-based inequalities and regional disparities were also assessed. At the national level, 14 out of the 17 health service indicators are projected to reach the target of 80% coverage by 2030. Across wealth quintiles, inequalities were observed amongst most indicators with richer groups obtaining more coverage than their poorer counterparts. Subnational analysis revealed while all regions will achieve the 80% coverage target with high probabilities for prevention services, the same cannot be applied to treatment services. In 2015, the proportion of households that suffered catastrophic health payments and impoverishment at a threshold of 25% non-food expenditure were 1.9% (95%CrI: 0.9-3.5) and 0.4% (95%CrI: 0.2-0.8), respectively. These are projected to reduce to less than 0.5% by 2030. Inequality measures and subnational assessment revealed that catastrophic expenditure experienced by wealth quintiles and regions are not equal. Significant improvements were seen in both health service coverage and financial risk protection as a result of NHIS. However, inequalities across wealth quintiles and at the subnational level continue to be cause of concerns. Further efforts are needed to narrow these inequality gaps.

scientific communication and education↗

bioRxiv2018

Multi-objective formulation of MSA for phylogeny estimation (Do phylogeny-aware measures guide towards better phylogenetic tree?)

Multiple sequence alignment (MSA) is a basic step in many analyses in computational biology, including predicting the structure and function of proteins, orthology prediction and estimating phylogenies. The objective of MSA is to infer the homology among the sequences of chosen species. Commonly, the MSAs are inferred by optimizing a single function or objective. The alignments estimated under one criterion may be different to the alignments generated by other criteria, inferring discordant homologies and thus leading to different evolutionary histories relating the sequences. In recent past, researchers have advocated for the multi-objective formulation of MSA, to address this issue, where multiple conflicting objective functions are being optimized simultaneously to generate a set of alignments. However, no theoretical or empirical justification with respect to a real-life application has been shown for a particular multi-objective formulation. In this study, we investigate the impact of multi-objective formulation in the context of phylogenetic tree estimation. Employing multi-objective metaheuristics, we demonstrate that trees estimated on the alignments generated by multi-objective formulation are substantially better than the trees estimated by the state-of-the-art MSA tools, including PASTA, MUSCLE, CLUSTAL, MAFFT etc. We also demonstrate that highly accurate alignments with respect to popular measures like sum-of-pair (SP) score and total-column (TC) score do not necessarily lead to highly accurate phylogenetic trees. Thus in essence we ask the question whether a phylogeny-aware metric can guide us in choosing appropriate multi-objective formulations that can result in better phylogeny estimation. And we answer the question affirmatively through carefully designed extensive empirical study. As a by-product we also suggest a methodology for primary selection of a set of objective functions for a multi-objective formulation based on the association with the resulting phylogenetic tree.

bioinformatics↗