Menjelajahi berkas fastq

Berkas fastq biasanya berisi ribuan hingga jutaan read, dan ukurannya dapat sangat besar! Untuk latihan ini, Anda akan menggunakan sub-sampel fastq kecil berisi 500 read, yang mudah dimuat ke memori dan dapat dibaca seluruhnya menggunakan fungsi readFastq().

Berkas sekuens asli berasal dari Arabidopsis thaliana, disediakan oleh UC Davis Genome Center. Nomor aksesinya adalah SRR1971253 dan diunduh dari Sequence Read Archive (SRA). Berkas ini berisi DNA dari jaringan daun, digabungkan dan disekuensing menggunakan Illumina HiSeq 2000. Sekuens ini merupakan single-read dengan panjang 50 pasangan basa (bp).

fqsample adalah objek ShortReadQ dan memuat informasi tentang read, skor kualitas, dan id. Sekarang giliran Anda untuk menjelajahinya!

Latihan ini adalah bagian dari kursus

Pengantar Bioconductor di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Load ShortRead
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# Print fqsample
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Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Bioconductor di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you will get hands-on with Bioconductor. Bioconductor is the specialized repository for bioinformatics software, developed and maintained by the R community. You will learn how to install and use bioconductor packages. You'll be introduced to S4 objects and functions, because most packages within Bioconductor inherit from S4. Additionally, you will use a real genomic dataset of a fungus to explore the BSgenome package.

Exercise 1: Introduction to the Bioconductor Project Exercise 2: Bioconductor version Exercise 3: BiocManager to install packages Exercise 4: The role of S4 in Bioconductor Exercise 5: S4 class definition Exercise 6: Interaction with classes Exercise 7: Introducing biology of genomic datasets Exercise 8: Discovering the yeast genome Exercise 9: Partitioning the yeast genome Exercise 10: Available genomes

Biostrings are memory efficient string containers. Biostring has matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. How efficient you can become by using the right containers for your sequences? You will learn about alphabets, and sequence manipulation by using the tiny genome of a virus.

Exercise 1: Introduction to Biostrings Exercise 2: Exploring the Zika virus sequence Exercise 3: Biostrings containers Exercise 4: Manipulating Biostrings Exercise 5: Sequence handling Exercise 6: From a set to a single sequence Exercise 7: Subsetting a set Exercise 8: Common sequence manipulation functions Exercise 9: Why are we interested in patterns?Exercise 10: Searching for a pattern Exercise 11: Finding Palindromes Exercise 12: Finding a conserved region within six frames Exercise 13: Looking for a match

The IRanges and GenomicRanges packages are also containers for storing and manipulating genomic intervals and variables defined along a genome. These packages provide infrastructure and support to many other Bioconductor packages because of their enriching features. You will learn how to use these containers and their associated metadata, for manipulation of your sequences. The dataset you will be looking at is a special gene of interest in the human genome.

Exercise 1: IRanges and Genomic Structures Exercise 2: IRanges Exercise 3: Constructing IRanges Exercise 4: Interacting with IRanges Exercise 5: Gene of interest Exercise 6: From tabular data to Genomic Ranges Exercise 7: GenomicRanges accessors Exercise 8: ABCD1 mutation Exercise 9: Human genome chromosome X Exercise 10: Manipulating collections of GRanges Exercise 11: A sequence window Exercise 12: Is it there?Exercise 13: More about ABCD1 Exercise 14: How many transcripts?Exercise 15: From GRangesList object into a GRanges object

ShortRead is the package for input, manipulation and assessment of fasta and fastq files. You can subset, trim and filter the sequences of interest, and even do a report of quality. An extra bonus towards the last exercises will give you the tools for parallel quality assessment, wink, wink Rqc. Exciting enough, for this you will use plant genome sequences!

Exercise 1: Berkas sekuens Exercise 2: Mengapa fastq?Exercise 3: Membaca berkas Exercise 4: Menjelajahi berkas fastq

Latihan Saat Ini

Exercise 5: Ekstrak sampel dari file fastq Exercise 6: Kualitas sekuens Exercise 7: Menjelajahi kualitas sekuens Exercise 8: Plot kualitas basa Exercise 9: Coba plot frekuensi nukleotida Anda sendiri Exercise 10: Pencocokan dan penyaringan Exercise 11: Memfilter read secara langsung!Exercise 12: Menghapus duplikasi Exercise 13: Lebih banyak penyaringan!Exercise 14: Penilaian berganda Exercise 15: Memplot kualitas rata-rata per siklus Exercise 16: Pengantar Bioconductor