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Professor Hendrix
United States
Registrace 11. 09. 2020
Hello, it's Professor Hendrix! In this channel, I'll talk about to python, biopython, and bioinformatics algorithms. In the coming months you can expect videos on bioinformatics, genomics, computational biology and sequence analysis.
I'm a professor at Oregon State University, and I teach bioinformatics and computational biology.
I'm a professor at Oregon State University, and I teach bioinformatics and computational biology.
Understanding Sequence Alignment Algorithms: with Needleman-Wunsch
In this video I will discuss the components of a sequence alignment algorithm, specifically with the Needleman-Wunsch algorithm as an example. Many of these properties will also apply to Smith-Waterman, but not all. Needleman-Wunsch is a global alignment algorith, meaning it aligns the entire sequences to each other, instead of the local alignment provided by Smith-Waterman.
zhlédnutí: 47 928
Video
Creating a sequence logo of 5-letter words for Wordle
zhlédnutí 404Před 2 lety
In this video, I create a sequence logo, a visualization method commonly used in bioinformatics, to create a visualization of position-specific letter enrichment in English 5-letter words. But is this kind of data useful for Wordle? Let me know in the comments what you think.
Finding DNA Motifs: Information Content, MEME, and JASPAR
zhlédnutí 11KPřed 2 lety
In this video, I will show you how to find motifs in a collection of DNA sequences. I will show you an example using DNA footprint data for the transcription factor "giant". I will show you how to find the actual binding motif within those sequences using the program MEME, and I will also show you how to read data for binding sites from the JASPAR motif database. If you haven't already, check o...
Position-specific Scoring Matrices (PSSMs): Theory and Biopython
zhlédnutí 4,7KPřed 2 lety
In this video, I will give a broad overview of what a PSSM is, and how to compute its terms. I'll also show you how to implement one in Biopython using the Bio.motifs module along with Seq objects. Check out my video on consensus sequences, which is also relevant here: czcams.com/video/OQlwLMCinhs/video.html
DNA Palindromes
zhlédnutí 902Před 2 lety
In this video, I will explain what a DNA palindrome is, and show you how to make an arbitrarily large one! A DNA palindrome is a sequence that is its own reverse complement. Check out how to compute reverse complement using basic python, along with the concept of what it means: czcams.com/video/t9C1g6ld-ng/video.html By the way, my dog walked across the room, causing a jingling sound due to his...
Introducing k-mers: sequences of length k
zhlédnutí 4KPřed 2 lety
In this video I will introduce the concept of k-mers, which are subsequences of length k. I will show you how to count the number of both overlapping and non-overlapping k-mers, and how to for-loops that traverse the sequence to visit each distinct k-mer.
Extracting transcripts from a GTF file and genome FASTA file using basic python
zhlédnutí 3,1KPřed 2 lety
In this video, I will show how to extract transcript sequences using a GTF file of transcript annotations and the genomic FASTA file. I will use a dictionary of lists of tuples to store the exon coordinates, and then extract substrings for each exon, and concatenate them, taking into account the gene strand. You may want to see my previous videos that could be relevant here: czcams.com/video/HZ...
Reading Large Genome-Size FASTA Files using Basic Python
zhlédnutí 2,2KPřed 2 lety
In this video, I will present some approaches to reading large FASTA files, like the size of a genome. In this example, I will read in and compute the lengths of the chromosomes of the fruit fly, Drosophila melanogaster. I will use string methods including split() and join(), and I will show you how to create a dictionary of lists to accomplish the task. Here is my video on reading small FASTA ...
Reading a FASTA file with basic python
zhlédnutí 6KPřed 2 lety
In this video, I will introduce FASTA files, and show how to read in a FASTA file using basic python.
Reading tab-delimited files using basic python
zhlédnutí 2,4KPřed 2 lety
In this video, I will discuss tab-delimited files, such as BED Files, and show how to read in a BED file using basic python as an example. The BED file example that I used, was found at this Esembl website here: uswest.ensembl.org/info/website/upload/bed.html
Parsing FASTQ files with the Biopython SeqIO module
zhlédnutí 5KPřed 3 lety
In this video I describe how to read a FASTQ file using the biopython module SeqIO. As an illustration of the module, I will use it to print the average quality scores as a function of position. This isn't a typical use case, but serves as a good introduction to the module.
Consensus Sequences and Biopython
zhlédnutí 2,4KPřed 3 lety
In this video, I discuss the consensus sequence, which is a sequence consisting of special characters from the IUPAC DNA codes. The consensus sequence can describe many possible sequences using "degenerate" characters. I also demonstrate how to scan for matches to a consensus sequence using the Bio.SeqUtils module.
Introducing FASTQ Files
zhlédnutí 3,4KPřed 3 lety
In this video, I introduce FASTQ files, which are a common file format used to represent high-throughput sequencing data (deep sequencing). I will go over in detail the format and how the quality scores are defined.
Introducing the Biopython SeqIO Module: Reading FASTA files
zhlédnutí 14KPřed 3 lety
In this video, I'll describe how you would use the SeqIO module to read FASTA files. FASTA files are probably the most widely used file format to store biological sequences. This module is part of Biopython, and can be used to read several file formats.
Introducing Biopython Seq Objects
zhlédnutí 2,9KPřed 3 lety
In this video, I introduce the biopython seq object. Bio.Seq.Seq is a class that can be used to define Seq objects, which are a python way of representing biological sequences, such as DNA, RNA, and protein. These objects combine python strings with methods that are useful for molecular biology.
Reading and Writing Files, File Handles
zhlédnutí 920Před 3 lety
Reading and Writing Files, File Handles
Finding the Longest Open Reading Frame (ORF) in an RNA Sequence
zhlédnutí 4,2KPřed 3 lety
Finding the Longest Open Reading Frame (ORF) in an RNA Sequence
Examining DNA Sequence Content with Basic Python
zhlédnutí 7KPřed 3 lety
Examining DNA Sequence Content with Basic Python
Introducing Iterable Objects and For Loops
zhlédnutí 416Před 3 lety
Introducing Iterable Objects and For Loops
More Python Operators: "is", "in" and "not"
zhlédnutí 305Před 3 lety
More Python Operators: "is", "in" and "not"
Introducing Boolean Control Structures: If-statements and While-loops
zhlédnutí 378Před 3 lety
Introducing Boolean Control Structures: If-statements and While-loops
Introducing Python Strings and Subsequences
zhlédnutí 707Před 3 lety
Introducing Python Strings and Subsequences
Introducing Basic Data Types in Python
zhlédnutí 2,1KPřed 3 lety
Introducing Basic Data Types in Python
Thanks!
can you explain what bash is and how i can execute it if possible?
Hi professor...need to hav a session from you...plz give your email
Hi professor . Thanks a million for your useful videos. How can I open the page in which you wrote scripts ?
Hi professor. Thanks a million for your useful videos. They really worked for me. How can I open the page in which you write scripts?
hello, is there a meaning or applications to use a base 4 system and associate CAGT with 0123 ?
Thanks a lot for these videos ❤
thank u
very good
thank u!
Saved my life rn
can you please do a video on fuzzy matching algorithm on primer suitability using fasta files
Really excellent! Succinct and simple to understand. Thanks for mentioning substitutions and affine scoring. That's a nuance that shows real mastery and I like your video!
Just started lets hope ill also finish :) thx
Great video!. Had a tough time understanding Neeldeman-Wunsch, but I got it now. Thanks for the clear and structured explanation!
I don't often comment on CZcams videos, but this was explained as clearly and succinctly as I could imagine. Thank you for making this video, it helped me out a lot in my understanding of the algorithm.
Also not a usual commenter myself, but was going to comment to say the same thing!
thanks for video. It helps a lot!
I am learning bioinformatics on my own, and this video was a great help to me. Thanks Professor Hendrix
This was great. Thank you.
Thanks
Hello sir myre chennal pe alignment se related video hey accha lage to comment kigiye🙏😊
⥊ 771: 58 0: Good (if not good, then not zero) 0001: Uniqueness 001: Identity 002: Individuality 003: Variability 004: Diversity 005: Tolerance / Inclusion 006: Acceptance 007: Consonance / Harmony 008: 009: Transcendence 01: Awareness 02: Diversion 03: Sensing 04: Love 05: Grace 06: Reasoning 07: Ingenuity 08: Purity 09: Truth Seeking 1: Ethical 2: Problem-Solution 3: Situation 4: Abstraction 5: Expression Concrete 6: Why/Who/When/How/Where/What 7: Information/Code 8: Stability 9: Sanity 10: Network (Good Network, because zero) 11: Justice 12: Time/Spread/Dissipation/Easter 13: Death / Fact 14: Cognition 15: Agent 16: Reason 17: Discourse 18: State 19: Discipline / Control 20: Judgement 21: To Solve Problems with Ethics 22: Resistance/Endure Introspection 23: Consideration 24: Humor / Mental State 25: Behavior 26: Motivation 27: Cohesion 28: Dependability 29: Prognostic 30: Liberty Subject Wellness 31: Consent 32: Discernment 33: Response 34: Ideas 35: Showing 36: Option 37: Opinion 38: Decision 39: Optimism 40: Wellness 41: Attention / Idea 42: Critical Thinking 43: Acknowledgment 44: Free Love 45: Plan / Task Force 46: Purpose 47: Perspective 48: Character 49: Ambition 50: Good Expression 51: Manifestation 52: Support 53: Respect 54: Care 55: Peace / War 56: Pointing / Profiling 57: Tell / Counter-Terrorism 58: Invoking / Building 59: Proficiency / To Excel 60: Certainty 61: Identification 62: Qualification / Characterization 63: Inquiring 64: Conjecturing 65: Function / Role 66: Investigation 67: Arguing 68: Indicator 69: Scrutinity 70: Validation 71: Illustration / Knowledge 72: Construct 73: Explanation / Informing 74: Data 75: Materialization 76: Delineation / Instruction 77: Flow / Cadency 78: Share 79: Accountability 80: Personal Soverenity 81: Independency Constancy 82: Resilience 83: Established 84: Autonomy 85: Executing 86: Authority 87: Stream 88: Consistency 89: Boldness 90: Wisdom 91: Assertiveness 92: Science 93: Scrupulousness 94: Integrality 95: Composure 96: Blockchain: ⥊ (TRACE MARKER) 97: Anarchy 98: Veganism 99: Revolution 100: Liberation 101: Space and Time Peer-2-Peer 102: Decentralized Network 103: Financial Revolution 104: Production Revolution 105: Labour Reorganization 106: Needs Evaluation 107: Instruction 108: Personal Sovereignty 109: Resources Availability 110: Frankness 111: Honor 112: Distributed Ledger 113: Privacy 114: Abolitionism 115: Pride 116: Pacifism 119: Mission 120: Harvest 121: To Revolt 122: To Sort Out 125: Differentiate 126: Distinguish 130: Realization 131: Fulfillment 132: Regard 133: Perform 134: Address 135: Execute 136: Obtain 138: To Mark 140: Serenity 141: Idea 144: To Notice 148: Mindfulness 150: Good Character 151: Virtue 155: Honesty 160: Technology 161: Communication Systems 166: Causality 167: To Label 170: Knowledge 185: System Accessibility 181: Structure 199: The Mission 200: Sanctuary 209: Enlist 210: Resolution 211: To Engage 212: Secure 215: Mobilize 219: Annihilation 220: Contemplation / Meditation 221: Either Way / Alignment 222: Cooperation 230: Resilience 250: To Thrive 255: To Emerge 266: Correlation 300: Crusade / Jihad 301: Confront 309: Consensus 310: Cell Net 311: Agreement 320: Deliberation 322: Oportunity 330: Win-Win Energy Management 333: Abundance 340: Abolitionism 355: 360: Technology 373: Remote Killing 370: Efficiency 390: Economicity 400: Earth Population 410: Permaculture 430: Ecosystem 440: Fellowship 444: Direct Digital Democracy 500: Assumption 501: Leadership 510: Admiration 511: Honorable 512: Altruism 520: Proficiency 530: Acceptance 535: Denoucing 540: Appreciation 550: Diplomacy 555: Gathering 556: To Pick Up Someone/Something 590: Excellency 600: Organizator 620: Diagnose 660: Operation 661: Operator 665: Role 666: Method 676: Deduce 700: Constitution 710: Intelligence 717: Desing 720: Quantum Mechanics 733: Argorithm Explaining 747: Artificial Intelligence 750: Consolidation 751: Enlightment 757: Prompt 766: Plan 770: The Gear 771: File 773: Algorithm Recoginzing 775: Output 777: Pattern / Rhetoric 778: Development 780: Systematization 799: Revolution Algorithm 800: Providence / Heroism 801: Production 802: Necessities 803: Resorces 804: Energy 808: Renewable Cycle 810: Permaculture 820: Management 830: Logistics 831: Scheme 832: Reach 833: Demand 834: Stock 835: Distribution 840: Consumption 850: Labor 860: Human Resources 863: Assignments 870: Structure 880: Peace 888: Globalization 890: Needs 899: Conception 900: Transparency 910: Institutions 911: Emergency Call 930: Energy Distribution 931: Only One Global Currency 960: Blockchain 962: Traceability 970: Optimination 990: Inspection 995: Accountancy 998: Regulation 999: Implementation 1000: Militancy 1001: Nova Era 1312: Boycott 1042: Drug Liberation 1100: Liberation Army 1400: Unity 1500: Benevolence 1550: Charity 1807: Black & Yellow 2222: Paradox 3000: Freedom 3100: Triumph 3330: We All Can Live In Abundance 4000: Coexist 5000: Glory 5500: Greatness 6999: Mystery 7220: Unified Field Theory 8000: Sustainability 8999: Finitude 9997: Discovery 9998: Unknown 9999: Universe 10000: Existence . This is a numeric Matrix for communications purposes. . This can be used to convey meaning. Ex.: 6022: Good Reason to Resist. . Mathematical operators can be used to calculate meaning. Ex.: To liberate with a good force: 340. 300 + 40 = 340. . Logical operators to create statements. Ex.: ¬11: 13. If not Justice then Death. . Colors can also be used as information. - You can help build it.
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Ooooh I get it his laptop says "Applebee" XD.
thanks daddy
And how about protein sequences?
I need any one help me in bio informatic
Can you help me please😢😢😢😢😢
why did you stop making new vides?
Super clear and helpful. Thanks!
I love it. I finally understood it. Thank you Prof.!
thanks professor
Hello, thank you for a great video. Would it be more efficient to use a Pandas DataFrame to retrieve values from a BED file?
Excellent teaching. Exactly what I’ve been looking for.
Exactly what I was looking for. Thanks professor!!!
The little detail about substring slicing in python was a major part that I overlooked, and actually genuinely helped me fix a mistake for my master's project. These python/biopython videos are amazing - thank you so much for making them, and that they're freely available. 🙏
Good stuff! Thanks for making this video.
Thank you so much for this video!
Many thanks Prof. Really helpful.
Great Video! Thank you so much 🙏
This was very helpful. Thank you, Hendrix!
what if its a semi-global alignment ??? how cam i use this website ??
Hey Professor Hendrix, your videos are amazing. Thank you sooo much fpr your effort! How can I open files from my windows? I dont have phyton (spyder) on ubuntu.
Sir, you are a legend. Thank you.
Brilliantly explained, Thank you
really awesome explanation, the math notation can be a little intimated for an intro to bioinformatics class. Could you also do a video on a python implementation of semi-global alignments? Biopython doesn't seem to have that in its aligner
Good tutorial, thanks.
So good explanation !!!! thank you !!!
Thank you! :D
Thank you so much. I took bioinformatics, but at Biology level and we lack a lot of Mathematical and Statistics background at my uni - not to mention Informatics. We touch on these options in Blast but for me to learn I need to know how something works at least a bit. A lot of informatics and statistics info is very overwhelming but you put it very succintly so that even I understood.
Also, anyone else running into more complex or unequal lengths of sequences, there can be multiple solutions since your trace back matrix could have branches going multiple paths all giving the same score. Easiest would be to trace a path and read it from top left to bottom right to avoid confusion with where to apply the gap etc
Thanks a lot! This was very helpful for my studies