R bioinformatics statistical analysis

Perform quantitative RNAseq

use edgeR Estimate the differential expression | use edgeR Estimate the differential expression | Use powsimR Conduct efficacy analysis | Use GRanges Object to find uncommented transcriptional regions | Use bumphunter Look for areas that show high expression from the beginning | Differential peak analysis | Use SVA Estimate the batch effect | Use AllelicImbalance Look for allele specific expression | Draw and render RNAseq data

Use HTS Data search for genetic variation

Use VariantTools Find... In sequence data SNP And insertion missing | Predict the open read frame in the long reference sequence | Use karyoploteR Draw features on the genetic map | Look for alternative transcriptional subtypes | Use VariantAnnotation Select and classify variants | Extract information from genomic regions of interest | Look for and GWAS The phenotype and genotype of | Estimate the number of copies of the site of interest

Search for domains and Motifs of genes and proteins

Looking for a common motif DNA Motif | Use PFAM and bio3d | Look for protein domains | lookup InterPro Domain | Perform multiple comparisons of genes or proteins | Use DECIPHER Align genome length sequences | Machine learning is used to detect new features in proteins | Use bio3d Conduct 3D Structural protein alignment

Use SeqinR Retrieve genome sequence data :

for example , You learned how to learn from NCBI Web site retrieval has NCBI Login number NC_001477 Of DEN-1 Dengue virus genome sequence . To retrieve information with a specific NCBI Added sequence , You can use R function “getncbiseq()”, You first need to copy and paste it into R in ：

> getncbiseq <- function(accession)
  {
    
     require("seqinr") # this function requires the SeqinR R package
     # first find which ACNUC database the accession is stored in:
     dbs <- c("genbank","refseq","refseqViruses","bacterial")
     numdbs <- length(dbs)
     for (i in 1:numdbs)
     {
    
        db <- dbs[i]
        choosebank(db)
        # check if the sequence is in ACNUC database 'db':
        resquery <- try(query(".tmpquery", paste("AC=", accession)), silent = TRUE)
        if (!(inherits(resquery, "try-error")))
        {
    
           queryname <- "query2"
           thequery <- paste("AC=",accession,sep="")
           query(`queryname`,`thequery`)
           # see if a sequence was retrieved:
           seq <- getSequence(query2$req[[1]])
           closebank()
           return(seq)
        }
        closebank()
     }
     print(paste("ERROR: accession",accession,"was not found"))
  }

Will function getncbiseq() Copy and paste to R In the after , You can use it from NCBI Search sequence in nucleotide database , for example DEN-1 The sequence of dengue virus （ Login number NC_001477）：

> dengueseq <- getncbiseq("NC_001477")

Variable dengueseq It is a vector containing nucleotide sequence . Each element of the vector contains a nucleotide of the sequence . therefore , To print out a subsequence of the sequence , We just type in the vector dengueseq The name of , Then enter the square brackets containing these nucleotide indexes . for example , The following command prints DEN-1 The pre genome sequence of dengue virus 50 Nucleotides ：

> dengueseq[1:50]
[1] "a" "g" "t" "t" "g" "t" "t" "a" "g" "t" "c" "t" "a" "c" "g" "t" "g" "g" "a"
[20] "c" "c" "g" "a" "c" "a" "a" "g" "a" "a" "c" "a" "g" "t" "t" "t" "c" "g" "a"
[39] "a" "t" "c" "g" "g" "a" "a" "g" "c" "t" "t" "g"

Please note that ,dengueseq[1:50] It's a vector dengueseq The elements of , Its index is 1-50. These elements include DEN-1 Pre dengue virus sequence 50 Nucleotides .

Phylogenetic analysis and visualization

Use ape and treeio Read and write various tree formats | Use ggtree Quickly visualize a tree of many genes | Using tree space to quantify the distance between trees | Use ape Extract and process subtrees | Create a dot chart for alignment Visualization | Use phangorn Rebuild trees from the route

Macrogenomics

Use phyloseq Load hierarchical classification data | Use a meta encoder for sparse counting to correct for sample differences | Use dada2 Read the amplicon data from the original read | Using heat tree to visualize classification abundance in meta encoder | Calculate the sample diversity using the pure element | Split the sequence file into operable taxons

Proteomics from spectroscopy to annotation

Visually represent the original MS data | View proteomic data in the genome browser | Visualize the distribution of peptide hit counts to find thresholds | transformation MS Format to move data between tools | Use protViz Match the spectrum with the peptide for verification | Apply a quality control filter to the spectrum | Identify genomic sites that match peptides

Production release and Web Ready Visualization

Use ridgeplots Visualize multiple distributions | Create a color map for bivariate data | Represent relational data as a network | Use plotly Create interactive Web graphics | Use plotly Build a 3D drawing | Construct a circular genome map of multiple sets of data

Using databases and remote data sources

from BioMart Search for genes and genome annotations | Retrieve and use SNP | Obtain gene ontology information | from SRA/ENA Find experiments and readings in | Perform quality control and filtering on high-throughput sequence reads | Use an external program to complete the reading to reference comparison | Visually read the quality control chart of reference comparison

Useful statistical and machine learning methods

correction p Value to explain multiple assumptions | Generate an analog dataset representing the background | Learn groups in data and use kNN To classify | Use random forest prediction classes | Use SVM Prediction | Learning groups in data without prior information | Use random forests to identify the most important variables in the data | Use PCA Identify the most important variables in the data

Use Tidyverse and Bioconductor Programming

Make basic R The object is clean | Use nested data frames | Written for mutate Function of | Use... Programmatically Bioconductor class | Develop reusable workflows and reports | Use apply Series of functions

Build objects and packages for code reuse

Create simple S3 Object simplifies code | utilize S3 Class | Use S4 The system creates structured and formal objects | A simple way to package code for sharing and reuse | Use devtools Managed from GitHub Code for | Build unit test suites to ensure functionality | Work as you wish | Use Travis To keep code testing and updating

Source code

For details, please refer to - Yatu inter