Nicolas Jacquet

Introducing our new bioinformatics protocols

As a biologist, you probably know that the key to good data analysis is the selection and use of appropriate software. Owing to the increasing complexity of biological data, the number of tools typically required to perform an analysis is constantly growing, rendering the selection of software even more difficult. Indeed, finding the best series of tools that match your analysis criteria is challenging.   A protocol is a series of software presented in a logical order. With your data as a starting point, a protocol shows you the right pathway of tools to perform your analysis.   Until now, finding the optimal workflow was very challenging because bioinformatics analyses are difficult to decipher or remain buried in the masses of biomedical texts. To face this big …

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Choosing the right name for a bioinformatics tool

The name of your bioinformatics tool is the icing on the cake. You should be very attentive and feel concerned regarding this feature. While having a great name does not assure you to stand out, a bad appellation often results in a failure to reach your audience. The do’s and don’ts Don’t take a name that already exists Your tool is unique. As such, it deserves to have a sole name. Don’t try to copy the name of a famous tool or your favorite one. It can be detrimental for you. Indeed, people tend to compare products with each other, especially if they have the same title. They can be confused about the function of your tool.   omicX contains more …

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Semantic Web in life sciences

  The exponential growth of the number of biological studies over the past decade has created an enormous challenge to make effective use of the accumulated information. Owing to this size, simple web-style text search engines are often not yielding the best results and a lot of important information remains buried in the masses of text (Doms, 2005). Semantic technologies have been introduced for better question answering and faster literature exploration (Doms, 2008). They use ontologies to give an overview over large query results and guide scientists for discoveries.   Starting your research in biology but don’t know which platform to use to find impactful studies? Here is a list of useful resources you will love using to retrieve articles …

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How to identify the right tools for biological data analysis

In the biology big data context, managing the amount and diversity of data that experiments produce is a challenging task. Depending on the scope of your research, you probably spend a lot of your time searching for the right bioinformatics tools.   Like most, you probably have a general idea of how to analyze your data and have used more than one tool. If you’re working in a computational biology laboratory, you’ve probably heard the question “What is the best software for genome sequence alignment?” or “What algorithm is the standard for sequence alignment in genetics?” While BLAST is probably the most popular tool for this, there are lots of other tools for mining, and aligning biological data.  So choosing the right solution for …

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How to find bioinformatics jobs

As part of your market strategy, you should craft and maintain a professional online persona. Use a platform such as LinkedIn or ResearchGate to create a detailed profile emphasizing key skills and experience, and to link up with others in relevant organizations or fields of research (…) Use online technical forums to ask about skills and experience needed in an industry  (…) Taking part in such dialogues can make recruiters notice you and seek you out regarding prospective openings. Peter Fiske. Why scientists need to market themselves. Nature.   As a job seeker, you probably use solutions to find job opportunities in your field of expertise. It can be difficult and time consuming to manually vet the many options available …

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Snakemake: taking parallelization a step further

Written by Raoul Raffel from Bioinfo-fr.net, translated by Sarah Mackenzie.   Hello and welcome back to a new episode of the series of Snakemake tutorials, this one dealing with parallelization. If you missed the first episode introducing you to Snakemake for Dummies, check out the article to catch up on it before you read on. Here we are going to see how easy Snakemake makes it to parallelize data. The general idea revolves around cutting out the raw files from the start of your pipeline and then putting them back together after the calculation-intensive steps. We are also going to find out how to use a JSON configuration file. This file is the equivalent of a dictionary / hash table …

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Snakemake for dummies (or how to create a bioinformatics pipeline easily)

Written by Raoul Raffel from Bioinfo-fr.net, translated by Sarah MacKenzie.   If you haven’t already heard about this tool, you have certainly not read the article Formalising your protocols with Snakemake by Louise-Amélie Schmitt (maybe not surprising if you don’t understand French!). So, what are the advantages of rewriting your ready-to-go pipelines in Snakefile? The answer? Code readability, resource management, and reproducibility Once you are ready to publish your data, you will have to prepare yourself to explain to your future readers exactly how you have obtained your data in order to allow other bioinformaticians to take your raw data and reproduce the same results. This is a critical aspect of bioinformatics – indeed of all scientific research: reproducibility. Which …

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