A meta-genome sequencing and assembly preprocessing algorithm inspired by restriction site base composition

Bonham-Carter, Oliver; Ali, Hesham; Bastola, Dhundy

doi:10.1109/bibmw.2012.6470222

Cited by 6 publications

(7 citation statements)

References 14 publications

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“…In addition, we note that there is no longer a color pattern showing that Clostridium botulinum are closely related, as we saw in Figure 6. This result taken from our previous work in [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

“…There are ten unique recognition sites covering all four spectrum sets (shown in Figure 4). This graphic taken from our previous work in [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

“…The motifs of each set are made up by the permutations of one of the following words; AAATTT , AATTCG , CCGGAT or CCCGGG . This result taken from our previous work in [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

“… Sequence fragments are separated into groups (called,"bins") of relatedness by a quick pre-proccessing step. This graphic taken from our previous work in [ 33 ]. …”

Section: Introduction and Related Workmentioning

confidence: 99%

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A base composition analysis of natural patterns for the preprocessing of metagenome sequences

2013

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BackgroundOn the pretext that sequence reads and contigs often exhibit the same kinds of base usage that is also observed in the sequences from which they are derived, we offer a base composition analysis tool. Our tool uses these natural patterns to determine relatedness across sequence data. We introduce spectrum sets (sets of motifs) which are permutations of bacterial restriction sites and the base composition analysis framework to measure their proportional content in sequence data. We suggest that this framework will increase the efficiency during the pre-processing stages of metagenome sequencing and assembly projects.ResultsOur method is able to differentiate organisms and their reads or contigs. The framework shows how to successfully determine the relatedness between these reads or contigs by comparison of base composition. In particular, we show that two types of organismal-sequence data are fundamentally different by analyzing their spectrum set motif proportions (coverage). By the application of one of the four possible spectrum sets, encompassing all known restriction sites, we provide the evidence to claim that each set has a different ability to differentiate sequence data. Furthermore, we show that the spectrum set selection having relevance to one organism, but not to the others of the data set, will greatly improve performance of sequence differentiation even if the fragment size of the read, contig or sequence is not lengthy.ConclusionsWe show the proof of concept of our method by its application to ten trials of two or three freshly selected sequence fragments (reads and contigs) for each experiment across the six organisms of our set. Here we describe a novel and computationally effective pre-processing step for metagenome sequencing and assembly tasks. Furthermore, our base composition method has applications in phylogeny where it can be used to infer evolutionary distances between organisms based on the notion that related organisms often have much conserved code.

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Section: Resultsmentioning

confidence: 99%

“…There are ten unique recognition sites covering all four spectrum sets (shown in Figure 4). This graphic taken from our previous work in [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

“…The motifs of each set are made up by the permutations of one of the following words; AAATTT , AATTCG , CCGGAT or CCCGGG . This result taken from our previous work in [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

“… Sequence fragments are separated into groups (called,"bins") of relatedness by a quick pre-proccessing step. This graphic taken from our previous work in [ 33 ]. …”

Section: Introduction and Related Workmentioning

confidence: 99%

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A base composition analysis of natural patterns for the preprocessing of metagenome sequences

2013

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“…Because of the common hardships of applying limited computing resources to processing voluminous quantities of data, a statistical analysis is often appropriate [24,25]. Furthermore, frequency analysis is especially well suited for comparing large data sets and discovery, as it embraces convenient techniques of network analysis to ascertain natural patterns [26,27].…”

Section: Computing Frequenciesmentioning

confidence: 99%

A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays

Bonham-Carter

Thapa²,

From

et al. 2016

Brief Bioinform

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Post-translational modifications (PTMs) are important steps in the biosynthesis of proteins. Aside from their integral contributions to protein development, i.e. perform specialized proteolytic cleavage of regulatory subunits, the covalent addition of functional groups of proteins or the degradation of entire proteins, PTMs are also involved in enabling proteins to withstand and recover from temporary environmental stresses (heat shock, microgravity and many others). The literature supports evidence of thousands of recently discovered PTMs, many of which may likely contribute similarly (perhaps, even, interchangeably) to protein stress response. Although there are many PTM actors on the biological stage, our study determines that these PTMs are generally cast into organism-specific, preferential roles. In this work, we study the PTM compositions across the mitochondrial (Mt) and non-Mt proteomes of 11 diverse organisms to illustrate that each organism appears to have a unique list of PTMs, and an equally unique list of PTM-associated residue reaction sites (RSs), where PTMs interact with protein.Despite the present limitation of available PTM data across different species, we apply existing and current protein data to illustrate particular organismal biases. We explore the relative frequencies of observed PTMs, the RSs and general amino-acid compositions of Mt and non-Mt proteomes. We apply these data to create networks and heatmaps to illustrate the evidence of bias. We show that the number of PTMs and RSs appears to grow along with organismal complexity, which may imply that environmental stress could play a role in this bias.

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A content and structural assessment of oxidative motifs across a diverse set of life forms

Bonham-Carter

Pedersen

Bastola

2014

Computers in Biology and Medicine

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A meta-genome sequencing and assembly preprocessing algorithm inspired by restriction site base composition

Cited by 6 publications

References 14 publications

A base composition analysis of natural patterns for the preprocessing of metagenome sequences

A base composition analysis of natural patterns for the preprocessing of metagenome sequences

A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays

A content and structural assessment of oxidative motifs across a diverse set of life forms

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