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Next-Generation Sequencing data analysis

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Abhi Salunkhe Next Generation Sequencing Market to 2025 – Global Analysis and Forecasts by Product (Platforms, Services, Consumables); Service (Genome Sequencing, Exome Sequencing, Resequencing & Targeted Sequencing, Other Sequencing Technologies)
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The next generation sequencing market is estimated to grow at a CAGR of 21.1% during the forecast period from 2018 to 2025. The next generation sequencing market is estimated to reach US$ 26,501.25 Mn by 2025.
Next generation sequencing (NGS) is the technology which is used to align millions of small fragments of DNA simultaneously that are capable of processing multiple DNA sequences in parallel.
The major players operating in the market of next generation sequencing market include F. Hoffmann-La Roche AG, PerkinElmer, Inc., Oxford Nanopore Technologies, Ltd., Eurofins Scientific, Thermo Fisher Scientific, Beijing Genomics Institute, Agilent Technologies, Qiagen N.V., Macrogen, Inc., and Illumina, Inc. among others
savita Bangar Top 10 Companies In The Next Generation Sequencing (NGS) Market
The global NGS services market is expected to reach USD 2,921.4 million by 2023 supported by a CAGR of 20.1% during the forecast period of 2018 to 2023, says “Meticulous Research”. Visit On
The field of genomics has surpassed expectations over the past three decades due to massive changes in technology that allowed researchers to interrogate larger pieces of the human genome. The NGS technology has potential to become an ultimate geno typing platform for human identification. The capability of NGS led its application as forensic markers such as short tandem repeats (STR), mitochondrial and Y-chromosome haplotypes. Growing genomics research in academic and research institutes and outsourcing of genome testing in bio pharmaceutical & biotechnology industry to drive the adoption of NGS services, thus propelling the NGS services market.
David Langenberger The Low Quality Of Scientific Code
That's the harsh truth, but how can this problem be solved? Can it be solves? What's your experience?
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On the one hand, scientific code is produced by scientists, who are mostly interested in achieving high quality of their scientific publications. The quality is measured in the currency of the domain -- e.g. new cancer drug targets that guarantee good publications. The code is a mere means to get to the signal in the data but is not recognized as product by itself. The situation is promoted by languages like AWK and Bash that make it easy to produce unstructured spaghetti code but are just unbeatably efficient for solving certain simple tasks. Structured programming is impossible if even the concept of a function is unknown or forgotten (which is a real life example of mine).
On the other hand, scientific code is often highly tested on huge and diverse datasets. It is robust to the input data and things like computer node failure. Unfortunately, the tests are not repeatable and the robustness stems from code adaptation to solve immediate problems ad hoc (to meet some deadline or satisfy the PI).
Moving such code from science to production, e.g. to serve in a wider community, is at best hard. And there is simply no money for scientific programmers to re-programm everything. The deciders up (or in particular) at the highest level do not value software as a product of science and do rather invest their money in hardware that outdates quickly than in supporting people and projects -- something that would be needed to produce quality software.


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About the group: Next-Generation Sequencing data analysis

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