Computational power of transcriptome analysis

1.
transcriptome
refers to the collection of all mRNA procts transcribed in a certain species or specific cell in a certain physiological function state, including the limitation of time
and space
and is the inevitable link between
genome
genetic information and biological function
proteome
. The regulation of transcription level is the most studied and the most important regulation mode of organism
transcriptome sequencing using high-throughput technology is a fast and reliable method to obtain transcriptome information. Transcriptional expression analysis of mRNA, through obtaining the information of the genome transcription region of the research object, identifying the transcription
sites
, variable splicing, etc., its accurate counting method can be used for accurate quantitative analysis of genes.

2. What do you do first? Or transcriptome sequencing
I don't know much about the chip. It may be a closed system. At present, it's not very good for finding new transcripts
for transcriptome sequencing, the contaminated sequences were removed first, and then the fragments overlapped. Then each read is mapped to the genome to obtain go value, functional annotation, transcriptional level evaluation, functional enrichment and pathway analysis. If you are interested in the newly discovered transcripts, you can also make functional prediction and cellular localization of transcripts. I hope there are experts to evaluate it. If it's wrong, just point it out. OVER.

3. Sequencing raw data processing (quality cutting, contaminated sequence removal, etc.)
short sequence mapping to reference genome sequence
gene function annotation
gene structure analysis
variable cutting analysis
gene expression difference analysis (no less than two samples
predict new genes)

4. How to get the final means from two repeats of transcriptome sequencing 1. Distinguish biological repeats from technical repeats biological repeats: sample repeats, such as three mice, are processed at the same time, that is, three biological repeats. Technical repetition: generally three experiments, such as RNA extraction and real time for a piece of tissue. 2. The significance of setting up biological plication e to the advantages and high cost of the new generation sequencing technology, the importance of "biological plication" was once ignored. However, biological plication is very important for the design of sequencing experiments and the interpretation and analysis of experimental data. Biological plication: it can eliminate intra group error; biological plication can measure the degree of variation and enhance the reliability of the results; the more samples sequenced, the lower the background difference; outlier samples detected: the existence of abnormal samples will seriously affect the accuracy of sequencing results. Abnormal samples can be found and eliminated by calculating the correlation between samples.

5. If you just study the algorithm and make a prototype, of course, Python is much more elegant. But I don't think Perl and python are the most suitable choices for search engines

6. UniGene doesn't have a standard definition. The general concept is the gene sequence obtained after de rendancy. This sequence and other sequences are non rendant, that is, theoretically, other sequences are not the same gene represented by this sequence. Or different clusters are obtained after clustering, and the clusters are non rendant. Each cluster can be considered as a unique gene. The results obtained by different de rendancy and clustering methods can be called UniGene
in addition, NCBI also has the concept of UniGene. NCBI uses its own method to cluster sequences. It thinks that each class represents a unique gene, and gives a number: the name of the species. For example, an example of wheat: ta.191, which is also a commonly used UniGene

7. If you want to do a good job in data analysis, you need to read more books

1. Statistics and truth: how to use contingency
2. Google analytical classic analysis
3. Statistics: from data to conclusion (Wu Xi) 2nd Edition
4. Statistical data standardization method
several supplementary books:
1 The world of statistics
2. The truth of people's livelihood data
3. Statistical traps

8.

They represent different aspects of function as follows:

C (cellular component): subcellular localization of gene (non) coding procts

F (molecular function): molecular activity of gene procts

P (biological process): biological process and pathway composed of multiple gene procts

reference: Web links

9.

Transcriptome analysis refers to the analysis of all transcripts in a cell

in a broad sense, transcriptome refers to the collection of all transcripts in a cell under a certain physiological condition, including messenger RNA, ribosomal RNA, transport RNA and non coding RNA; In a narrow sense, it refers to the collection of all mRNA

transcriptome sequencing generally involves high-throughput sequencing of mature mRNA and ncRNA transcribed by RNA polymerase II purified with oligothymine (oligo DT)

compared with the traditional microarray hybridization platform, transcriptome sequencing can detect the overall transcriptional activity of any species without designing probes for known sequences in advance, providing more accurate digital signals, higher detection throughput and wider detection range, which is a powerful tool for in-depth study of transcriptome complexity

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extended data:

technical route of transcriptome sequencing:

sample requirements:

1. Sample purity requirements: the total rnaod value should be between 1.8 and 2.2; The ratio of 28s to 18S was more than 1.5

The concentration of total RNA was not less than 400 ng / UL; The total amount of sample is not less than 15ug; At present, the requirement of the latest sample library is reced to 1 UG, and the concentration is more than 50 ng / UL

The specific concentration, volume, preparation time, solvent name and species source of total RNA samples were provided. Please also attach QC data, including electrophoretic gel chart, spectrophotometer or nanodrop instrument test data. If multiple sample preparation is required, the samples required for multiple sample preparation shall be provided