A file in fasta format is probably the most common way to store sequence information. The format is very simple: the name of each sequence is
listed on a line beginning with a '>' (the sequence name is everything after the '>'), and the sequence associated with that name is listed on one or
more lines after the name line. For a more detailed description, see http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml. For the examples below,
program output will be shown for the following fasta-format file, called seqs. fa: human ORF Finding: Write a program find0rf s .py that takes the
name of a single fasta-format file from the command line. Your program should read DNA sequences from the fasta file, translate them into protein
sequences, and finally print out the protein sequence of all possible ORFs (open reading frames starting with a start codon and ending with one of
the three stop codons, with no stop codons in between]. You should look at all three reading frames on the forward strand (do not worry about
the three reading frames on the reverse complement). The ORFs your program will be able to find can range in length from 2 amino acids (a start
and a stop) to the entire fasta sequence record (of any length). There may be more than one ORF in each fasta sequence record, possibly in
different reading frames. You do not need to worry about outputting the ORFs in any specific order so long as your output includes all ORFs
present in the sequence. To get you started, think about tackling the problem as follows: Read sequences from the fasta file For each sequence,
translate the reading frames starting at the 1st, 2nd, and 3rd base in the sequence For each translated sequence, find any ORFs present in that
sequence Example program usage: python findOrfs.py seqs.fa ORFs in seqA - human: ORFs in seqC - gorilla: MTR ORFs in that sequence
A file in fasta format is probably the most common way to store sequence info...hwbloom410000
A file in fasta format is probably the most common way to store sequence information. The format is very simple: the name of each sequence is
listed on a line beginning with a '>' (the sequence name is everything after the '>'), and the sequence associated with that name is listed on one or
more lines after the name line. For a more detailed description, see http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml. For the examples below,
program output will be shown for the following fasta-format file, called seqs. fa: human Fasta files: Write a program sortFasta.py that accepts a
single command-line argument which is the name of a file in fasta format. First, it reads in the file of DNA sequences in fasta format and stores them
in memory. Then, your program should re-print the sequences in fasta format to the screen, sorting the sequences by sequence name. Hint: a
dictionary is a convenient data structure for holding the Example program usage
A file in fasta format is probably the most common way to store sequence info...hwbloom410000
A file in fasta format is probably the most common way to store sequence information. The format is very simple: the name of each sequence is
listed on a line beginning with a '>' (the sequence name is everything after the '>'), and the sequence associated with that name is listed on one or
more lines after the name line. For a more detailed description, see http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml. For the examples below,
program output will be shown for the following fasta-format file, called seqs. fa: human Fasta files: Write a program sortFasta.py that accepts a
single command-line argument which is the name of a file in fasta format. First, it reads in the file of DNA sequences in fasta format and stores them
in memory. Then, your program should re-print the sequences in fasta format to the screen, sorting the sequences by sequence name. Hint: a
dictionary is a convenient data structure for holding the Example program usage
You will write a Python program that finds all the ORFs in a genomicchandaronald
You will write a Python program that finds all the ORFs in a genomic sequence.
A genomic sequence has 6 reading frames, corresponding to the six possible ways of translating the sequence into three-letter codons. Frame 1 treats each group of three bases as a codon, starting from the first base. Frame 2 starts at the second base, and frame 3 starts at the third base. Frames 4, 5 and 6 are defined in a similar way, but refer to the opposite strand, which is the reverse complement of the first strand.
Specifications:
Write a Python program called orfs to find all the open reading frames (ORFs) in the input sequence.
INPUT: The program will take in as input a file, which will contain any number of DNA sequences in the FASTA format: - A line beginning with a ">" is the header line for the next sequence - All lines after the header contain sequence data. - There will be any number of sequences per file. - Sequences may be split over many lines. - Sequence data may be upper or lower case. - Sequence data may contain white space, which should be ignored.
Ask the user for the minimum ORF to search for. The default is 50, which means your program should print out all ORFs with at least 50 bases.
OUTPUT:
Print your output in FASTA format, with one header line for each ORF, followed by the DNA in the ORF. The header should be the same as the header in the input file, followed by a bar "|" followed by FRAME = POS = LEN = , where is the frame number (1-6)
is the genomic position of the start of the ORF (left end is base 1) is the length of the ORF (in bases) If N = 4, 5 or 6, then P should be a negative number that indicates the position of the start of the ORF from the right end of the sequence. The DNA in the ORF should be printed out with a space between each codon, and no more than 15 codons per line.
...
RNA Sequence data analysis,Transcriptome sequencing, Sequencing steady state RNA in a sample is known as RNA-Seq. It is free of limitations such as prior knowledge about the organism is not required.
RNA-Seq is useful to unravel inaccessible complexities of transcriptomics such as finding novel transcripts and isoforms.
Data set produced is large and complex; interpretation is not straight forward.
A file in fasta format is probably the most common way to store sequence info...hwbloom410000
A file in fasta format is probably the most common way to store sequence information. The format is very simple: the name of each sequence is
listed on a line beginning with a '>' (the sequence name is everything after the '>'), and the sequence associated with that name is listed on one or
more lines after the name line. For a more detailed description, see http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml. For the examples below,
program output will be shown for the following fasta-format file, called seqs. fa: human Fasta files: Write a program sortFasta.py that accepts a
single command-line argument which is the name of a file in fasta format. First, it reads in the file of DNA sequences in fasta format and stores them
in memory. Then, your program should re-print the sequences in fasta format to the screen, sorting the sequences by sequence name. Hint: a
dictionary is a convenient data structure for holding the Example program usage
A file in fasta format is probably the most common way to store sequence info...hwbloom410000
A file in fasta format is probably the most common way to store sequence information. The format is very simple: the name of each sequence is
listed on a line beginning with a '>' (the sequence name is everything after the '>'), and the sequence associated with that name is listed on one or
more lines after the name line. For a more detailed description, see http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml. For the examples below,
program output will be shown for the following fasta-format file, called seqs. fa: human Fasta files: Write a program sortFasta.py that accepts a
single command-line argument which is the name of a file in fasta format. First, it reads in the file of DNA sequences in fasta format and stores them
in memory. Then, your program should re-print the sequences in fasta format to the screen, sorting the sequences by sequence name. Hint: a
dictionary is a convenient data structure for holding the Example program usage
You will write a Python program that finds all the ORFs in a genomicchandaronald
You will write a Python program that finds all the ORFs in a genomic sequence.
A genomic sequence has 6 reading frames, corresponding to the six possible ways of translating the sequence into three-letter codons. Frame 1 treats each group of three bases as a codon, starting from the first base. Frame 2 starts at the second base, and frame 3 starts at the third base. Frames 4, 5 and 6 are defined in a similar way, but refer to the opposite strand, which is the reverse complement of the first strand.
Specifications:
Write a Python program called orfs to find all the open reading frames (ORFs) in the input sequence.
INPUT: The program will take in as input a file, which will contain any number of DNA sequences in the FASTA format: - A line beginning with a ">" is the header line for the next sequence - All lines after the header contain sequence data. - There will be any number of sequences per file. - Sequences may be split over many lines. - Sequence data may be upper or lower case. - Sequence data may contain white space, which should be ignored.
Ask the user for the minimum ORF to search for. The default is 50, which means your program should print out all ORFs with at least 50 bases.
OUTPUT:
Print your output in FASTA format, with one header line for each ORF, followed by the DNA in the ORF. The header should be the same as the header in the input file, followed by a bar "|" followed by FRAME = POS = LEN = , where is the frame number (1-6)
is the genomic position of the start of the ORF (left end is base 1) is the length of the ORF (in bases) If N = 4, 5 or 6, then P should be a negative number that indicates the position of the start of the ORF from the right end of the sequence. The DNA in the ORF should be printed out with a space between each codon, and no more than 15 codons per line.
...
RNA Sequence data analysis,Transcriptome sequencing, Sequencing steady state RNA in a sample is known as RNA-Seq. It is free of limitations such as prior knowledge about the organism is not required.
RNA-Seq is useful to unravel inaccessible complexities of transcriptomics such as finding novel transcripts and isoforms.
Data set produced is large and complex; interpretation is not straight forward.
Introduction to HMMER - A biosequence analysis tool with Hidden Markov Models Anax Fotopoulos
HMMER is used for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs).
Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST.
As part of this evolution in the HMMER software, we are committed to making the software available to as many scientists as possible. Earlier releases of HMMER were restricted to command line use. To make the software more accessible to the wide scientific community, we now provide servers that allow sequence searches to be performed interactively via the Web.
File Operation such as
Reading the file content
Writing the content to the file
Copying the content from one file to another file
Counting the number of character, words and lines of the file
Introduction to HMMER - A biosequence analysis tool with Hidden Markov Models Anax Fotopoulos
HMMER is used for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs).
Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST.
As part of this evolution in the HMMER software, we are committed to making the software available to as many scientists as possible. Earlier releases of HMMER were restricted to command line use. To make the software more accessible to the wide scientific community, we now provide servers that allow sequence searches to be performed interactively via the Web.
File Operation such as
Reading the file content
Writing the content to the file
Copying the content from one file to another file
Counting the number of character, words and lines of the file
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Home assignment II on Spectroscopy 2024 Answers.pdf
A file in fasta format is probably the most common way to store sequence information. The format is
1. Afile infasta format is probablythe most commonwayto store sequence information. The format is verysimple:the name ofeachsequence is
listed ona line beginningwitha '>' (the sequence name is everythingafter the '>'), and the sequence associated withthat name is listed onone or
more lines after the name line. For a more detailed description, see http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml. For the examples below,
programoutput willbe shownfor the followingfasta-format file, called seqs. fa:humanORF Finding:Write a programfind0rfs .pythat takes the
name ofa single fasta-format file fromthe command line. Your programshould read DNAsequences fromthe fasta file, translate theminto protein
sequences, and finallyprint out the proteinsequence ofallpossible ORFs (openreadingframes startingwitha start codonand endingwithone of
the three stop codons, withno stop codons inbetween]. Youshould look at allthree readingframes onthe forward strand (do not worryabout
the three readingframes onthe reverse complement). The ORFs your programwillbe able to find canrange inlengthfrom2 amino acids (a start
and a stop) to the entire fasta sequence record (ofanylength). There maybe more thanone ORF ineachfasta sequence record, possiblyin
different readingframes. Youdo not need to worryabout outputtingthe ORFs inanyspecific order so longas your output includes allORFs
present inthe sequence. To get youstarted, think about tacklingthe problemas follows:Read sequences fromthe fasta file For eachsequence,
translate the readingframes startingat the 1st, 2nd, and 3rd base inthe sequence For eachtranslated sequence, find anyORFs present inthat
sequence Example programusage:pythonfindOrfs.pyseqs.fa ORFs inseqA- human:ORFs inseqC - gorilla:MTRORFs inthat sequence
![Afile infasta format is probablythe most commonwayto store sequence information. The format is verysimple:the name ofeachsequence is
listed ona line beginningwitha '>' (the sequence name is everythingafter the '>'), and the sequence associated withthat name is listed onone or
more lines after the name line. For a more detailed description, see http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml. For the examples below,
programoutput willbe shownfor the followingfasta-format file, called seqs. fa:humanORF Finding:Write a programfind0rfs .pythat takes the
name ofa single fasta-format file fromthe command line. Your programshould read DNAsequences fromthe fasta file, translate theminto protein
sequences, and finallyprint out the proteinsequence ofallpossible ORFs (openreadingframes startingwitha start codonand endingwithone of
the three stop codons, withno stop codons inbetween]. Youshould look at allthree readingframes onthe forward strand (do not worryabout
the three readingframes onthe reverse complement). The ORFs your programwillbe able to find canrange inlengthfrom2 amino acids (a start
and a stop) to the entire fasta sequence record (ofanylength). There maybe more thanone ORF ineachfasta sequence record, possiblyin
different readingframes. Youdo not need to worryabout outputtingthe ORFs inanyspecific order so longas your output includes allORFs
present inthe sequence. To get youstarted, think about tacklingthe problemas follows:Read sequences fromthe fasta file For eachsequence,
translate the readingframes startingat the 1st, 2nd, and 3rd base inthe sequence For eachtranslated sequence, find anyORFs present inthat
sequence Example programusage:pythonfindOrfs.pyseqs.fa ORFs inseqA- human:ORFs inseqC - gorilla:MTRORFs inthat sequence](https://image.slidesharecdn.com/2670-180713141013/85/A-file-in-fasta-format-is-probably-the-most-common-way-to-store-sequence-information-The-format-is-1-320.jpg)
