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學生職業成果

43%

完成這些課程後已開始新的職業生涯

33%

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第 4 門課程(共 8 門)
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完成時間大約為10 小時
英語(English)
字幕:英語(English)

您將獲得的技能

Bioinformatics AlgorithmsAlgorithmsPython ProgrammingAlgorithms On Strings

學生職業成果

43%

完成這些課程後已開始新的職業生涯

33%

通過此課程獲得實實在在的工作福利
可分享的證書
完成後獲得證書
100% 在線
立即開始,按照自己的計劃學習。
第 4 門課程(共 8 門)
可靈活調整截止日期
根據您的日程表重置截止日期。
完成時間大約為10 小時
英語(English)
字幕:英語(English)

提供方

约翰霍普金斯大学 徽標

约翰霍普金斯大学

教學大綱 - 您將從這門課程中學到什麼

內容評分Thumbs Up97%(2,974 個評分)Info
1

1

完成時間為 4 小時

DNA sequencing, strings and matching

完成時間為 4 小時
19 個視頻 (總計 112 分鐘), 7 個閱讀材料, 2 個測驗
19 個視頻
Lecture: Why study this?4分鐘
Lecture: DNA sequencing past and present3分鐘
Lecture: Genomes as strings, reads as substrings5分鐘
Lecture: String definitions and Python examples3分鐘
Practical: String basics 7分鐘
Practical: Manipulating DNA strings 7分鐘
Practical: Downloading and parsing a genome 6分鐘
Lecture: How DNA gets copied3分鐘
Optional lecture: How second-generation sequencers work 7分鐘
Optional lecture: Sequencing errors and base qualities 6分鐘
Lecture: Sequencing reads in FASTQ format4分鐘
Practical: Working with sequencing reads 11分鐘
Practical: Analyzing reads by position 6分鐘
Lecture: Sequencers give pieces to genomic puzzles5分鐘
Lecture: Read alignment and why it's hard3分鐘
Lecture: Naive exact matching10分鐘
Practical: Matching artificial reads 6分鐘
Practical: Matching real reads 7分鐘
7 個閱讀材料
Welcome to Algorithms for DNA Sequencing10分鐘
Pre Course Survey10分鐘
Syllabus10分鐘
Setting up Python (and Jupyter)10分鐘
Getting slides and notebooks10分鐘
Using data files with Python programs10分鐘
Programming Homework 1 Instructions (Read First)10分鐘
2 個練習
Module 120分鐘
Programming Homework 114分鐘
2

2

完成時間為 3 小時

Preprocessing, indexing and approximate matching

完成時間為 3 小時
15 個視頻 (總計 114 分鐘), 1 個閱讀材料, 2 個測驗
15 個視頻
Lecture: Boyer-Moore basics8分鐘
Lecture: Boyer-Moore: putting it all together6分鐘
Lecture: Diversion: Repetitive elements5分鐘
Practical: Implementing Boyer-Moore 10分鐘
Lecture: Preprocessing7分鐘
Lecture: Indexing and the k-mer index10分鐘
Lecture: Ordered structures for indexing8分鐘
Lecture: Hash tables for indexing7分鐘
Practical: Implementing a k-mer index 7分鐘
Lecture: Variations on k-mer indexes9分鐘
Lecture: Genome indexes used in research9分鐘
Lecture: Approximate matching, Hamming and edit distance6分鐘
Lecture: Pigeonhole principle6分鐘
Practical: Implementing the pigeonhole principle 9分鐘
1 個閱讀材料
Programming Homework 2 Instructions (Read First)10分鐘
2 個練習
Module 220分鐘
Programming Homework 212分鐘
3

3

完成時間為 2 小時

Edit distance, assembly, overlaps

完成時間為 2 小時
13 個視頻 (總計 92 分鐘), 1 個閱讀材料, 2 個測驗
13 個視頻
Lecture: Solving the edit distance problem12分鐘
Lecture: Using dynamic programming for edit distance12分鐘
Practical: Implementing dynamic programming for edit distance 6分鐘
Lecture: A new solution to approximate matching9分鐘
Lecture: Meet the family: global and local alignment10分鐘
Practical: Implementing global alignment 8分鐘
Lecture: Read alignment in the field4分鐘
Lecture: Assembly: working from scratch2分鐘
Lecture: First and second laws of assembly8分鐘
Lecture: Overlap graphs8分鐘
Practical: Overlaps between pairs of reads 4分鐘
Practical: Finding and representing all overlaps 3分鐘
1 個閱讀材料
Programming Homework 3 Instructions (Read First)10分鐘
2 個練習
Module 320分鐘
Programming Homework 38分鐘
4

4

完成時間為 2 小時

Algorithms for assembly

完成時間為 2 小時
13 個視頻 (總計 83 分鐘), 1 個閱讀材料, 2 個測驗
13 個視頻
Lecture: The shortest common superstring problem8分鐘
Practical: Implementing shortest common superstring 4分鐘
Lecture: Greedy shortest common superstring7分鐘
Practical: Implementing greedy shortest common superstring 7分鐘
Lecture: Third law of assembly: repeats are bad5分鐘
Lecture: De Bruijn graphs and Eulerian walks8分鐘
Practical: Building a De Bruijn graph 4分鐘
Lecture: When Eulerian walks go wrong9分鐘
Lecture: Assemblers in practice8分鐘
Lecture: The future is long?9分鐘
Lecture: Computer science and life science5分鐘
Lecture: Thank yous 43
1 個閱讀材料
Post Course Survey10分鐘
2 個練習
Programming Homework 48分鐘
Module 414分鐘

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關於 基因组数据科学 專項課程

With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome. This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy. This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work. To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit....
基因组数据科学

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