![]() Third, companies may have their specific expertise and capabilities in areas such as data analysis or machine learning.Second, companies may want to protect their intellectual property and differentiate their products from competitors.First, NGS data processing software complements their NGS sequencing products and services.So why do most prominent players have their proprietary solutions for NGS data processing? There are several reasons why companies such as Illumina, Qiagen, Bio-Rad, and others have their proprietary solutions: There are several key reasons for that, namely: On the contrary, software or a platform is almost necessary when it comes to large-scale NGS data processing. Therefore, they usually rely on existing software and/or platform solutions from one of the sequencing providers or other biotech companies that have built-in and validated pipelines that enable more or less automated data processing and exploration. In most cases, this is not something most Life scientists are trained to do. You have to know how to set up the parameters, validate outputs on test samples, extract information and finally, use visualization tools. However, keep in mind that this requires skills and a deep understanding of bioinformatics. Why? Because you can access various tools and software packages via GitHub or even build your pipelines, you would not require any licensed software to perform the analysis. large scale), where to store data (raw and processed), type of NGS data, and level of expertise required to execute the analysis.įor example, in internal small-scale data processing, it often makes sense to use individual tools and software packages locally, especially if you are accustomed to using the command line. Most notably, how much data you have to process (small scale vs. Should you use open-source tools or software packages (e.g., pipeline with some basic steps), build a custom workflow and process locally, or use off-the-shelf solutions from one of the licensed software or platform providers? It all comes down to several factors when a decision has to be made. So how should one approach NGS data analysis? For example, tools/pipelines developed by Biotech companies to cover their needs and/or enable them to differentiate from the competition. However, for some specific use cases or to have more efficient tools from a performance standpoint, those might not be accessible as open-source. It is also important to note that the majority of the tools are available as open-source code and thus can be used by anyone and would cover most of the use cases. For example, some tools may be more efficient at aligning reads to a reference genome (e.g., STAR, Salmon, Bowtie, etc.), while others may be better at identifying genetic variants (e.g., HISAT2, etc.) or quantifying gene expression (e.g., Salmon, Kallisto, DESeq, etc.). Since NGS data is complex, numerous tools have been developed in the last two decades that enable NGS data processing.
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