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Meta-analysis of biological literature : Explained

The new cool word to biological realm “meta-analysis” can be better understood by understanding the meaning of first half of the term; META, meaning data of data..

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It’s a fine Monday morning, and the new intern finds his way to the laboratory of biological data mining procedures. His brief interview with the concerned scientist has allowed him to have very limited understanding of the subject….Upon his arrival he is greeted with a humongous corpus of mixed articles, say some 4000, and he is required to assemble specific information out of the data set, by diligently scrutinizing the components of each article.

Well, the situation could be frightening to a purely wet lab working biologist, but a man who has had any exposure to the real power of file handling with any programming language will know how to let a simple few lines of code do his bidding.

So what is meta-analysis about?

The new cool word to biological realm “meta-analysis” can be better understood by understanding the meaning of first half of the term; META, meaning  data of data, thence making meta-analysis an analysis of already published data by simply rearranging it, sorting it, and trying to find hidden patters out of published literature.

By most rudimentary means, meta-analysis can be achieved by reading the corpus of research and review articles concerning a particular topic which may be as wide a whole Eukaryotic genome or may be narrowed down to phyla, groups, species may be a specific disease or even any gene in particular. Where on one part we try to narrow down to disease or gene, one must also realize biological systems are most complex to date and present day computer simulations fail to rival the complexity with equal efficiency, so any analysis narrowed down to gene must also consider that the gene may very well be found in multiple organisms and thus may present considerably high amount of results irrelevant to the study.

A rigorous manual inspection of program sorted data is required to sort out such entries. Since meta-analysis relies heavily on statistical studies of data, researchers tend to rely on programming languages such as Stata and R to write their specific codes for analyses, R unlike Stata is free, produces publication quality outputs and provides a plethora of packages, of which a few provide programs like PDF miner, PubMed miner etc used for accessing PubMed database, these packages contain codes to access the database and extract all information off them with a command based interface for huge data sets at once cutting down manual efforts and time taken to achieve the task.

All praises sent, the method has its own fair share of drawbacks and issues. The current query system to NCBI and sister organizations fail to acknowledge synonymous terms and treats them as individual entities not linked to any, but only in association with the length of query items made alongwith. A robust query system is needed to enhance the results, and make the whole concept more efficient.

Need of the hour is to engage more resources into developing well-structured and somewhat intelligent query systems which can truly acknowledge the gene-names and abbreviations, scientific and English names of organisms and also the variations of presenting names of techniques involved.  

Manish is section editor at Bioinformatics Review. His interest includes core bioinformatics and wet lab work. He has been a part of various laboratories across India including IGIB

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Algorithms

MOCCA- A New Suite to Model cis- regulatory Elements for Motif Occurrence Combinatorics

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MOCCA- A New Suite to Model cis- regulatory Elements for Motif Occurrence Combinatorics

cis-regulatory elements are DNA sequence segments that regulate gene expression. cis-regulatory elements consist of some regions such as promoters, enhancers, and so on. These regions consist of specific sequence motifs. (more…)

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vs_Analysis.py: A Python Script to Analyze Virtual Screening Results of Autodock Vina

Dr. Muniba Faiza

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VS-Analysis: A Python Script to Analyze Virtual Screening Results of Autodock Vina

The output files obtained as a result of virtual screening (VS) using Autodock Vina may be large in number. It is difficult or quite impossible to analyze them manually. Therefore, we are providing a Python script to fetch top results (i.e., compounds showing low binding affinities). (more…)

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How to search motif pattern in FASTA sequences using Perl hash?

Dr. Muniba Faiza

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Here is a simple Perl script to search for motif patterns in a large FASTA file with multiple sequences.

(more…)

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How to read fasta sequences from a file using PHP?

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Here is a simple function in PHP to read fasta sequences from a file. (more…)

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How to read fasta sequences as hash using perl?

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This is a simple Perl script to read a multifasta file as a hash. (more…)

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BETSY: A new backward-chaining expert system for automated development of pipelines in Bioinformatics

Dr. Muniba Faiza

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Bioinformatics analyses have become long and difficult as it involves a large number of steps implemented for data processing. Bioinformatics pipelines are developed to make this process easier, which on one hand automate a specific analysis, while on the other hand, are still limited for investigative analyses requiring changes to the parameters used in the process. (more…)

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Algorithm and workflow of miRDB

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As mentioned in the previous article, Micro RNAs (miRNAs) are the short endogenous RNAs (~22 nucleotides) and originate from the non-coding RNAs [1], produced in single-celled eukaryotes, viruses, plants, and animals [2]. They play significant roles in various biological processes such as degradation of mRNA [3]. Several databases exist storing a large amount of information about miRNAs, one of such databases miRBase [4] was explained in the previous article, today we will explain the algorithm of miRDB [5,6], another database for miRNA target prediction. (more…)

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miRBase: Explained

Dr. Muniba Faiza

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Micro RNAs (miRNAs) are the short endogenous RNAs (~22 nucleotides) and originate from the non-coding RNAs [1], produced in single-celled eukaryotes, viruses, plants, and animals [2]. miRNAs are capable of controlling homeostasis [2] and play significant roles in various biological processes such as degradation of mRNA and post-translational inhibition through complementary base pairing [3].  (more…)

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Prediction of biochemical reactions catalyzed by enzymes in humans

Dr. Muniba Faiza

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There are many biological important enzymes which exist in the human body, one of them is Cytochrome P450 (CyP450) enzymes which are mostly considered in drug discovery due to their involvement in the majority (75%) of drug metabolism [1]. Therefore, various in-silico methods have been applied to predict the possible substrates of CyP 450 enzymes [2-4]. Recently, an in-silico model has been developed to predict the potential chemical reactions mediated by the enzymes present in humans including CyP450 enzymes [5]. (more…)

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A new high-level Python interface for MD simulation using GROMACS

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The roots of the molecular simulation application can be traced back to physics where it was applied to simplified hard-sphere systems [1]. This field of molecular simulation study has gained a lot of interest since then and applied to perform simulations to fold small protein at multi-microsecond scale [2-4], predict functional properties of receptors and to capture the intermediate transitions of the complex [5], and to study the movement and behavior of ligand in a binding pocket and also to predict interactions between receptors and ligands [6,7]. (more…)

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Machine learning in prediction of ageing-related genes/proteins

Dr. Muniba Faiza

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Ageing has a great impact on human health, when people’s age advance towards 80 years, approximately half of the proteins in the body get damaged through oxidation. The chemical degradations occurring in our body produce energy by the consumed food via oxidation in the presence of oxygen. (more…)

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Simulated sequence alignment software: An alternative to MSA benchmarks

Dr. Muniba Faiza

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In our previous article, we discussed different multiple sequence alignment (MSA) benchmarks to compare and assess the available MSA programs. However, since the last decade, several sequence simulation software have been introduced and are gaining more interest. In this article, we will be discussing various sequence simulating software being used as alternatives to MSA benchmarks. (more…)

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Benchmark databases for multiple sequence alignment: An overview

Dr. Muniba Faiza

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Multiple sequence alignment (MSA) is a very crucial step in most of the molecular analyses and evolutionary studies. Many MSA programs have been developed so far based on different approaches which attempt to provide optimal alignment with high accuracy. Basic algorithms employed to develop MSA programs include progressive algorithm [1], iterative-based [2], and consistency-based algorithm [3]. Some of the programs incorporate several other methods into the process of creating an optimal alignment such as M-COFFEE [4] and PCMA [5]. (more…)

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ab-initio prediction of protein structure: An introduction

Dr. Muniba Faiza

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We have heard a lot about the ab-initio term in Bioinformatics, which could be difficult to understand for newbies in the field of bioinformatics. Today, we will discuss in detail what ab-initio is and what are the applicable methods for it. (more…)

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Intrinsically disordered proteins’ predictors and databases: An overview

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Intrinsically unstructured proteins (IUPs) are the natively unfolded proteins which must be unfolded or disordered in order to perform their functions.  They are commonly referred to as intrinsically disordered proteins (IDPs) and play significant roles in regulating and signaling biological networks [1]. IDPs are also involved in the assembly of signaling complexes and in the dynamic self-assembly of membrane-less nuclear and cytoplasmic organelles [1]. The disordered regions in a protein can be highly conserved among the species in respect of both the composition and the sequence [2]. (more…)

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An introduction to the predictors of pathogenic point mutations

Dr. Muniba Faiza

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Single nucleotide variation is a change in a single nucleotide in a sequence irrespective of the frequency of the variation. Single nucleotide variants (SNVs) play a very important role in causing several diseases such as the tumor, cancer, etc. Many efforts have been made to identify the SNVs which were initially based on identifying non-synonymous mutations in coding regions of the genomes. (more…)

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SparkBLAST: Introduction

Dr. Muniba Faiza

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The basic local alignment search tool (BLAST) [1,2] is known for its speed and results, which is also a primary step in sequence analysis. The ever-increasing demand for processing huge amount of genomic data has led to the development of new scalable and highly efficient computational tools/algorithms. For example, MapReduce is the most widely accepted framework which supports design patterns representing general reusable solutions to some problems including biological assembly [3] and is highly efficient to handle large datasets running over hundreds to thousands of processing nodes [4]. But the implementation frameworks of MapReduce (such as Hadoop) limits its capability to process smaller data. (more…)

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Role of Information Theory, Chaos Theory, and Linear Algebra and Statistics in the development of alignment-free sequence analysis

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Sequence alignment is customary to not only find similar regions among a pair of sequences but also to study the structural, functional and evolutionary relationship between organisms. Many tools have been discovered to achieve the goal of alignment of a pair of sequences, separately for nucleotide sequence and amino acid sequence, BLOSSUM & PAM [1] are a few to name. (more…)

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Bioinformatics Challenges and Advances in RNA interference

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RNA interference is a post-transcriptional gene regulatory mechanism to down-regulate the gene expression either by mRNA degradation or by mRNA translation inhibition. The mechanism involves a small partially complementary RNA against the target gene. To perform the action, it also requires a class of dedicated proteins to process these primary RNAs into mature microRNAs. The guide sequence determines the specificity of the miRNA. Therefore, the knowledge of the guide sequence is crucial for predicting its targets and also exploiting the sequence to create a new regulatory circuit. In this short review, we will briefly discuss the role and challenges in miRNA research for unveiling the target prediction by bioinformatics and to foster our understanding and applications of RNA interference. (more…)

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Systems pharmacology and drug development

Dr. Muniba Faiza

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Systems pharmacology is an emerging area in the field of medicinal chemistry and pharmacology which utilizes systems network to understand drug action at the organ and organism level. It applies the computational and experimental systems biology approaches to pharmacology, which includes network analyzes at multiple biological organization levels facilitating the understanding of both therapeutic and adverse effects of the drugs. Nearly a decade ago, the term systems pharmacology was used to define the drug action in a specific organ system such as reproductive pharmacology [1], but to date, it has been expanded to different organ and organism levels [2]. (more…)

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