Multiple sequence alignment modeling: methods and applications

Chatzou, Maria; Magis, Cedrik; Chang, Jia‐Ming; Kemena, Carsten; Bussotti, Giovanni; Erb, Ionas; Notredame, Cédric

doi:10.1093/bib/bbv099

Cited by 171 publications

(130 citation statements)

References 134 publications

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“…Nucleotide conservation scores can be difficult to compute since multi-sequence alignment is computationally challenging for a large number of sequences. 30,31 Instead of using multi-sequence alignment, we estimate the conservation of each nucleotide in the reference genome through the relative frequency of k-mers spanning the nucleotide in a set of genome assemblies (Method section 4.e). The conservation scores are computed in three steps ( Figure 2).…”

Section: Resultsmentioning

confidence: 99%

A novel core genome approach to enable prospective and dynamic monitoring of infectious outbreaks

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Chamarthi

et al. 2018

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Whole-genome sequencing is increasingly adopted in clinical settings to identify pathogen transmissions. Currently, such studies are performed largely retrospectively, but to be actionable they need to be carried out prospectively, in which samples are continuously added and compared to previous samples. To enable prospective pathogen comparison, genomic relatedness metrics based on single nucleotide differences must be consistent across time, efficient to compute and reliable for a large variety of samples. The choice of genomic regions to compare, i.e., the core genome, is critical to obtain a good metric.We propose a novel core genome method that selects conserved sequences in the reference genome by comparing its k-mer content to that of publicly available genome assemblies. The conserved-sequence genome is sample set-independent, which enables prospective pathogen monitoring. Based on clinical data sets of 3436 S. aureus, 1362 K. pneumoniae and 348 E. faecium samples, we show that the conserved-sequence genome disambiguates same-patient samples better than a core genome consisting of conserved genes. The conserved-sequence genome confirms outbreak samples with high accuracy: in a set of 2335 S. aureus samples, it correctly identifies 44 out of 45 outbreak samples, whereas the conserved gene method confirms 38 out of 45 outbreak samples.

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Section: Resultsmentioning

confidence: 99%

A novel core genome approach to enable prospective and dynamic monitoring of infectious outbreaks

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Kolde

Chamarthi

et al. 2018

Preprint

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“…A second limitation is that our study only examines relatively small numbers of sequences compared with the many thousands or tens of thousands that can be considered in some evolutionary and functional studies (Smith et al 2011;Chatzou et al 2015). The transition from 16 to 64 sequences provides some insight into the relative behavior of the methods, but the transition to very large numbers of sequences is a qualitatively different problem due to the computational constraints it imposes and how different MSA methods and phylogenetic methods address those problems.…”

Section: Discussionmentioning

confidence: 99%

“…The problems with this step arise from at least two sources (Chatzou et al 2015). First, the most widely used MSA methods (MSAMs) cannot cope with statistical uncertainty and only return a single point estimate of the MSA with no indication of its reliability.…”

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confidence: 99%

Phylogenetic Tree Estimation With and Without Alignment: New Distance Methods and Benchmarking

Bogusz

Whelan

2016

Syst Biol

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Abstract.-Phylogenetic tree inference is a critical component of many systematic and evolutionary studies. The majority of these studies are based on the two-step process of multiple sequence alignment followed by tree inference, despite persistent evidence that the alignment step can lead to biased results. Here we present a two-part study that first presents PaHMMTree, a novel neighbor joining-based method that estimates pairwise distances without assuming a single alignment. We then use simulations to benchmark its performance against a wide-range of other phylogenetic tree inference methods, including the first comparison of alignment-free distance-based methods against more conventional tree estimation methods. Our new method for calculating pairwise distances based on statistical alignment provides distance estimates that are as accurate as those obtained using standard methods based on the true alignment. Pairwise distance estimates based on the two-step process tend to be substantially less accurate. This improved performance carries through to tree inference, where PaHMM-Tree provides more accurate tree estimates than all of the pairwise distance methods assessed. For close to moderately divergent sequence data we find that the two-step methods using statistical inference, where information from all sequences is included in the estimation procedure, tend to perform better than PaHMM-Tree, particularly full statistical alignment, which simultaneously estimates both the tree and the alignment. For deep divergences we find the alignment step becomes so prone to error that our distance-based PaHMM-Tree outperforms all other methods of tree inference. Finally, we find that the accuracy of alignment-free methods tends to decline faster than standard two-step methods in the presence of alignment uncertainty, and identify no conditions where alignment-free methods are equal to or more accurate than standard phylogenetic methods even in the presence of substantial alignment error. [Alignment-free; distance-based phylogenetics; pair Hidden Markov Models; phylogenetic inference; statistical alignment.]

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“…Some parameters are considered during the collection of the samples, such as, unaligned FASTA format is selected from the database while the sequences include tree ordering and lower case letters. It is known that Multiple Sequence Alignment (MSA) (14) and it plays an important role in a comparative functional and structural study in biological sciences. In this regard, T-Coffee alignment algorithm (15) is used to align the POU2F1 family sequences, where T-Coffee is a progressive alignment method, produces a list of pairwise alignment to conduct the MSA.…”

Section: Evolutionary Trait Based On Sequence Complexitymentioning

confidence: 99%

Studying POU2F1 to Unveil the Structural Facet for Pan-Cancer Analysis Considering the Functional Annotations and Sequence-Structure Space Paradigm

Dey

Sen

Maulik

2019

Preprint

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POU domain class 2 homebox 1 (POU2F1) is largely known for its transcription factor(TF) activity. Due to its association with different types of malignancies, POU2F1 becomes one of the key factors in pan-cancer analysis. However, in spite of understanding it as a potential drug target, none of the drug has been designed till date due to its extreme dynamicity. In this article, we have proposed a three fold comprehensive framework for understanding the structural conservation and co-variation of POU2F1. Firstly, a gene regulatory network based non-pathogenic and pathogenic study have been performed to understand the strong association between cancers and POU2F1. After that, based on evolutionary sequence space study, the comparative sequential dynamicity of the protein members of POU domain family has been observed mostly between non-human and human samples. Subsequently, the reciprocity effect of the residual co-variation has been identified through direct coupling analysis. Along with that, the structure of POU2F1 has been analysed depending on quality assessment and normal mode based structure network. Comparing the sequence and structure space information, the most significant set of residue viz., 3,9,13,17,20,21,28,35,36 have been identified as structural facet for stable binding. It is observed that targeting these residues can help to prevent the monomeric aggregations. This study demonstrates-the observed malleability of POU2F1 is one of the prime reason behind its functional multiplicity in terms of protein moonlighting. SIGNIFICANCE POU2F1 is one of the important TFs associated with several malignancies. In spite of orderly stable structure in primitive family proteins, this protein is highly unstable at the monomeric stage. Previous studies show that POU2F1 has a clear association with 121 different diseases. Therefore, it becomes one of the important links in the pan-cancer study. Interestingly, TFs can be considered as a potential drug target. However, it is hardly possible to control the extreme dynamicity of TFs. In this regard, protein moonlighting plays an important role. We have tried to provide a theoretical frame to build understanding on POU2F1. The study provides pathogenic and non-pathogenic connections of the protein in terms of comprehensive and precise Gene Regulatory Network. Subsequently, the instability has been unveiled and the structural facet has been identified. Finally, the specified set of function for a suitable session to target the protein is analyzed based on protein moonlighting properties.

show abstract

Multiple sequence alignment modeling: methods and applications

Cited by 171 publications

References 134 publications

A novel core genome approach to enable prospective and dynamic monitoring of infectious outbreaks

A novel core genome approach to enable prospective and dynamic monitoring of infectious outbreaks

Phylogenetic Tree Estimation With and Without Alignment: New Distance Methods and Benchmarking

Studying POU2F1 to Unveil the Structural Facet for Pan-Cancer Analysis Considering the Functional Annotations and Sequence-Structure Space Paradigm

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