How to use bioinformatics methods for data mining

major bioinformatics databases and their classification and characteristics; the use of bioinformatics databases is discussed; .Keywords: database; Bioinformatics; sequence alignment; Data mining;

When I first started writing articles, I always had a sense of dread. There are a lot of thoughts in my head, but when I really want to express them, I always feel blank. Although I read a stomach full of academic articles, the words written down are like ** or prose, and they can't be made piecemeal by piece.

There are so many terms that I don't know how to put them together. This anxiety will be more or less at first, and it will take time and practice to make up for it.

Writing an academic article is a bit like programming. When I first started learning a programming language, I didn't dare to write scribbling without understanding the grammar rules. Even if I wrote it, the compiler kept reporting errors, and when I was looking for the reason, I felt that the syntax rules were so complicated, and I didn't know what the reason was after looking for a long time.

But when you finally master this programming language, it is easy to write **, the definition is naturally defined, and the function is used where it is time to use it. Uncomplicated processes can be written while you think. Complex process, as long as the logic is thought through, everything seems to be completed naturally.

At this time, when I look back at the grammar rules, I will feel that it is good to have rules, and if ** is written in prose, it will go crazy to debug.

Research writing is also a technical job, and it also has to go through a process of practice makes perfect, first of all, you need to be familiar with the basic way of academic writing. On the basis of being able to convey information with basic accuracy, it is natural to start developing your own writing style. Don't try to copy your tutor's writing style at the beginning, because the level of understanding of the problem is definitely not up to it, and it would be too far-fetched to force someone else's style to imitate it.

At the same time, when writing an article, just think about the article as a paragraph, don't think too complicated. Don't think of piling up academic jargon as the goal of writing, successful writers don't want to present things in a complex way, but to convey their message accurately. Just like the ** that is written, the main thing is to run correctly, and whether it looks good or not is written is the next step requirement.

For example, when readers read science and technology news or articles, they will not be in the way of appreciating art, but can quickly extract their own useful information from them.

In the early stages of writing, it is most useful to collect feedback from colleagues or peers, which can help you identify various problems and then know what to improve in subsequent writing. In the later stage of writing, if it is in English**, you can ask a professional **editing company such as Yingnonge to provide language assistance Collapse.

The role of bioinformatics cannot be overstated, nor can it be denied that the combination of bioinformatics methods and experiments has made great contributions. The essence of bio-informatics analysis and experimental analysis is the same, that is, qualitative and quantitative analysis of the data generated by the experiment, and then further verification to obtain relatively stronger conclusions. It is not possible to take out the biological information alone and remove the experiment and say that the biological information is a huge discovery.

To give a simple example, the Talen Genomics Technology Group Macroslow Technique, which has been popular for a long time since 2009, is a back-to-back article, one using experimental methods and the other using bioinformatics methods, which found that Tal proteins can specifically bind DNA sequences. Various gene articles later, as well as protein structure elucidation. The collapse of CRISPER technology has replaced Talen, which is a later story.

The Human Genome Project, the Proteome Project, the Cancer Genome Project, and other high-throughput projects cannot be said to be absolute discoveries of bioinformatics, but it can be said that bioinformatics has played an irreplaceable role. Bioinformatics has penetrated into most biomedical research, from genetics, epigenetics, molecular biology, evolutionary biology, structural biology and other basic fields. It is also rapidly used in animal and plant breeding, disease diagnosis, and drug development.

In other words, when everything that requires high throughput to solve biological problems is in old age, bioinformatics is about to come into play. How to play biology + Internet, rely on bioinformatics! As the most flexible, most morphological, most inclusive, and dynamically changing direction in the field of biology (the biggest feeling of participating in the bioinformatics conference is that the people who do bioinformatics have very different backgrounds, studying physics, computers, experiments, and evolution are all doing their own bioinformatics), I believe that one day, basic bioinformatics skills including programming, and routine sequencing data analysis will be a necessary skill for every biological researcher.

Before the 60s of the last century, the understanding of evolution was that genetic mutations were either good or bad, and with the slow accumulation of gene sequence data, some people began to use sequence comparison to analyze the sequence of the same gene (ortholog) between different species. Zuckerkandl and Paul found that the magnitude of the sequence difference between species was always proportional to the time of species differentiation (extrapolated from fossil evidence) (i.e., the molecular clock). If the acceptance of mutations is determined solely by natural selection, then we should not see this phenomenon, but rather that the size of the difference in sequence is directly proportional to the size of the difference in the living environment and the strength of natural selection (the strength of natural selection for large populations).

This discovery later paved the way for the introduction of the doctrine of neutrality (kimura). The theory of neutrality states that only a few genetic mutations are good or bad, most are neutral, and whether they can be accepted or not is completely random, so the longer the evolutionary time, the more differences will be randomly accumulated, so there will be the phenomenon of molecular clocks. Now at the genotypic level, everyone has accepted the doctrine of neutrality.

In addition, sequencing and sequence comparison were not as easy as they are today. The sequence alignment algorithm is also a very important part of bioinformatics, and there is a lot to say, but it is not enough.

We should consider biological information in a broad sense. Any experimental data is the information we collect, and from the experimental results to the biological conclusions, it is often necessary to use statistical knowledge to effectively understand the data. Monter's discovery of a 3-to-1 separation ratio, and Morgan's discovery of the third law are inseparable from the interpretation of data.

The discovery of long and short non-coding RNAs is also inseparable from the analysis of data discriminatory travel. If bioinformatics is understood as a tool discipline for the study of genetics, genomics, and complex diseases, rather than as a specialized science, it is easy to realize that many important biological discoveries cannot be made without bioinformatics. lncrna。

Through genome sequencing, it has been found that the amount of mRNA used to translate proteins is much smaller than that of transcribed mRNA, which was once regarded as a by-product of transcription, but now it has been found to have a very complex and diverse regulatory pattern and ability. At present, it has gradually become a research hotspot in genomics.

How to start from scratch and grasp the origin.

Physical informatics analysis.

In just over a decade, ZHI has formed a number of research directions, and some of the main research focuses are briefly introduced under DAO. Such as gene expression profiling analysis, metabolic network analysis; Gene chip design and proteomics data analysis have gradually become emerging and important research fields in bioinformatics. In terms of disciplines, disciplines derived from bioinformatics include structural genomics, functional genomics, comparative genomics, proteinology, pharmacogenomics, traditional Chinese medicine genomics, tumor genomics, molecular epidemiology, and environmental genomics, which have become important research methods for systems biology. It is not difficult to see from the development that genetic engineering has entered the post-genomic era.

We also have a clear understanding of the possible misinformation in bioinformatics, such as machine learning, and mathematics.

Today's world is very different, and in the field of life science research, everything is starting to enter the era of big data, whether it is DNA sequences, microscopy**, or mass spectrometry data, researchers increasingly need to collect, integrate, process and interpret this huge amount of information.

For many biologists, this is not easy to do, and traditional scientific training focuses on the basic principles of science and experimental methods, rather than computer programming and statistics, so many researchers do not know how to deal with these problems when they find themselves confronted with large amounts of data.

There is no shortage of ready-made calculation tools, and many of them are free, but they are still a bit difficult for laymen. Often, researchers need to have a deep understanding of these programs that are not user-friendly in order to run, and this requires deep knowledge of computational operations.

As a result, researchers have to write their own programs to process repeatable and verifiable information when conducting big data research. However, these processes also need to be handled with care, and if you don't pay attention to making mistakes, you can jeopardize the data itself.