GcatWiki - User contributions [en]

Notes on the life of Dr. Campbell

2016-04-28T18:17:32Z

Nibalanda: Created page with "- Grew up in Little Rock, Arkansas --> land of sophistication in 8th grade there was a parents day (for students to bring in their kids) education not valued, poor people, --..."

- Grew up in Little Rock, Arkansas --> land of sophistication
in 8th grade there was a parents day (for students to bring in their kids)

education not valued, poor people, --> basically, arkansas sucks
but dr c was dedicated and became a lifeguard to get him out of the hood
then worked in a tile sweatshop for 2 years --> stacking tiles and slapping them
paid for college by paving parking lots --> earned the nickname "tar pants"

went to davidson--> bio major '84

grad school - big egos, small mines

substitute teacher for a little: wore his overalls -> kids troll dr c with pencil drop and dr c beats them senseless to make an example of them
once smoked with kids in the bathroom

read out of africa in africa

once put an evil chameleon on his face

taught at shikokho in 87 -> one guy had a major crush on Dr. C's wife so Dr. C took his wife home to the states

once had a jigger in his toe and it laid eggs in Dr. C and now Dr. C is half man half jigger

made little kids cry in africa just by looking at him

realized he made it when he was sitting on a roof in kenya

sat in on buddhas first sermon in india (circa 400 bc)

**students sit back as Dr. C inspires them**

Nick Balanda

2016-04-28T17:40:31Z

Nibalanda:

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[[Notes on the life of Dr. Campbell]]

Nick Balanda

2016-03-31T17:52:39Z

Nibalanda:

[[Group 1 intestines]]

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2016-03-24T18:50:10Z

Nibalanda: Created page with " Nick Balanda took approach1.xlsx file and added % change column removed all genes that had fpkm value of 0 in either fed or unfed and put into a new file geneswithzeros..."

[[Nick Balanda]]

took approach1.xlsx file and added % change column

removed all genes that had fpkm value of 0 in either fed or unfed and put into a new file geneswithzeros.xlsx
--in this file, changed all zeros to .001 so that we could see relative change in expression levels of genes that were not expressed at all in either fed or unfed snakes

dylan sent a copy of go ids associated with hippo signaling pathway from cytoscape:
-- should move forward by looking at genes with any of the above go ids and see if they have significant up/down regulation

found out that the hippo signaling pathway is THE pathway of interest

Nick Balanda

2016-03-24T18:45:04Z

Nibalanda:

[[Group 1 intestines]]

[[Notes]]

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Nick Balanda

2016-03-10T18:38:48Z

Nibalanda:

[[Group 1 intestines]]

[[Notes]]

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Nick Balanda

2016-03-08T18:45:45Z

Nibalanda:

[[Group 1 intestines]]

[[Notes]]

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2/25

2016-02-25T19:18:07Z

Nibalanda:

[[Nick Balanda]]

python script: link gene name with GO ids..
#Had to change “ to --> no space

#Had to change ‘ to --> _prime

#Had to change _prime_prime ('') --> to ^

2/23

2016-02-25T18:40:28Z

Nibalanda: Created page with "Nick Balanda"

[[Nick Balanda]]

2/25

2016-02-25T18:40:18Z

Nibalanda: Created page with "Nick Balanda"

[[Nick Balanda]]

Nick Balanda

2016-02-25T18:40:08Z

Nibalanda:

[[Group 1 intestines]]

[[Notes]]

[[2/4]]

[[2/9]]

[[2/11]]

[[2/18]]

[[2/23]]

[[2/25]]

Nick Balanda

2016-02-23T18:40:41Z

Nibalanda:

[[Group 1 intestines]]

[[Notes]]

[[2/4]]

[[2/9]]

[[2/11]]

[[2/18]]

[[2/23]]

2/18

2016-02-18T18:47:47Z

Nibalanda:

[[Nick Balanda]]

New progress:

new clustering. able to create csv file of genes in a cluster

2/18

2016-02-18T18:43:46Z

Nibalanda: Created page with "Nick Balanda"

[[Nick Balanda]]

Nick Balanda

2016-02-18T18:43:35Z

Nibalanda:

[[Group 1 intestines]]

[[Notes]]

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[[2/9]]

[[2/11]]

[[2/18]]

2/11

2016-02-11T18:49:35Z

Nibalanda: Created page with "'''Assignment:''' Find way to associate gene ontology terms with gene names."

'''Assignment:'''

Find way to associate gene ontology terms with gene names.

Nick Balanda

2016-02-11T18:48:56Z

Nibalanda:

[[Group 1 intestines]]

[[Notes]]

[[2/4]]

[[2/9]]

[[2/11]]

2/9

2016-02-11T18:48:40Z

Nibalanda:

[[Nick Balanda]]

'''What is Gene Clustering?'''

- grouping genes together based on similar proteins they code for
-allows for presentation of similar genes together, analyze representative data points within big data (draw patterns), make predictions

---not having to sort through whole data set

-many algorithms

-gene expression data:

a) often comparative expression levels

b) consider that repression is as significant as induction (increase in expression)

b1) might use log scale to represent this!

b2) could mean co-regulation or correlation

linkage methods:

difference between average point in cluster and point in question, minimum distance between point in cluster and point in question,, etc

'''hierarchical clustering:'''

-join two most similar genes, repeat until all genes have been clustered (no gene left behind-- starts at +1 correlation, end at -1)

---cutting the tree-- dividing gene clusters into groups by drawing line through hierarchical tree and acknowledging groups left behind

'''k-means clustering:'''

specify how many clusters to form, groups each gene to one of ''k'' different clusters to maximize similarity

'''supervised clustering:'''

find all genes w/ expresion patterns matching "fill in the blank:" (all like this particular gene that we found upregulated)

'''quality clustering (QT clust)'''

each gene builds its own cluster based on genes that are most similar to it (repeat for every gene)

-come up with rule for "best cluster" (because each cluster likely overlaps many others)

--default in this case is to remove biggest cluster, then the next biggest, so on...

'''there is no perfect answer for clustering, you have to experiment based on some biological meaning in order to draw most accurate conclusions'''

'''clustering info/practice:'''

[http://gcat.davidson.edu/DGPB/clust/home.htm]

2/9

2016-02-09T19:55:34Z

Nibalanda:

'''What is Gene Clustering?'''

- grouping genes together based on similar proteins they code for
-allows for presentation of similar genes together, analyze representative data points within big data (draw patterns), make predictions

---not having to sort through whole data set

-many algorithms

-gene expression data:

a) often comparative expression levels

b) consider that repression is as significant as induction (increase in expression)

b1) might use log scale to represent this!

b2) could mean co-regulation or correlation

linkage methods:

difference between average point in cluster and point in question, minimum distance between point in cluster and point in question,, etc

'''hierarchical clustering:'''

-join two most similar genes, repeat until all genes have been clustered (no gene left behind-- starts at +1 correlation, end at -1)

---cutting the tree-- dividing gene clusters into groups by drawing line through hierarchical tree and acknowledging groups left behind

'''k-means clustering:'''

specify how many clusters to form, groups each gene to one of ''k'' different clusters to maximize similarity

'''supervised clustering:'''

find all genes w/ expresion patterns matching "fill in the blank:" (all like this particular gene that we found upregulated)

'''quality clustering (QT clust)'''

each gene builds its own cluster based on genes that are most similar to it (repeat for every gene)

-come up with rule for "best cluster" (because each cluster likely overlaps many others)

--default in this case is to remove biggest cluster, then the next biggest, so on...

'''there is no perfect answer for clustering, you have to experiment based on some biological meaning in order to draw most accurate conclusions'''

'''clustering info/practice:'''

[http://gcat.davidson.edu/DGPB/clust/home.htm]

2/9

2016-02-09T19:54:56Z

Nibalanda:

'''What is Gene Clustering?'''

- grouping genes together based on similar proteins they code for
-allows for presentation of similar genes together, analyze representative data points within big data (draw patterns), make predictions

---not having to sort through whole data set

-many algorithms

-gene expression data:

a) often comparative expression levels

b) consider that repression is as significant as induction (increase in expression)

b1) might use log scale to represent this!

b2) could mean co-regulation or correlation

linkage methods:

difference between average point in cluster and point in question, minimum distance between point in cluster and point in question,, etc

'''hierarchical clustering:'''

'''clustering info/practice:'''

http://gcat.davidson.edu/DGPB/clust/home.htm

-join two most similar genes, repeat until all genes have been clustered (no gene left behind-- starts at +1 correlation, end at -1)

---cutting the tree-- dividing gene clusters into groups by drawing line through hierarchical tree and acknowledging groups left behind

'''k-means clustering:'''

specify how many clusters to form, groups each gene to one of ''k'' different clusters to maximize similarity

'''supervised clustering:'''

find all genes w/ expresion patterns matching "fill in the blank:" (all like this particular gene that we found upregulated)

'''quality clustering (QT clust)'''

each gene builds its own cluster based on genes that are most similar to it (repeat for every gene)

-come up with rule for "best cluster" (because each cluster likely overlaps many others)

--default in this case is to remove biggest cluster, then the next biggest, so on...

'''there is no perfect answer for clustering, you have to experiment based on some biological meaning in order to draw most accurate conclusions'''

2/9

2016-02-09T19:35:38Z

Nibalanda: Created page with "'''What is Gene Clustering?''' - grouping genes together based on similar proteins they code for -allows for presentation of similar genes together, analyze representative da..."

Nick Balanda

2016-02-09T18:56:04Z

Nibalanda:

[[Group 1 intestines]]

[[Notes]]

[[2/4]]

[[2/9]]

2/4

2016-02-04T19:54:55Z

Nibalanda:

[[Nick Balanda]]

What are we trying to accomplish?!?!?

1. what is it we want? perfect outcome? How do we get there?
- associate gene oncology with genes from DeSeq.

2. What are we going to do with each of our 12 data sets to evaluate?
-validate sample types - are 6 liver samples comparable liver samples? intestines? use list of housekeeping genes.

-----------------

What triggers cascade?
-transcription activator protein already there? or is g-protein activating transcription protein that turns on keystone transcription factor?
-maybe first look for transcript for transcription factor! -> bc this might be the initiator of the cascade

2/4

2016-02-04T19:29:44Z

Nibalanda:

Notes

2016-02-04T19:28:35Z

Nibalanda:

[[Nick Balanda]]

Snakes 1,2,3 not fed
Snakes 4,5,6 fed
--info in excel file
RNA seq in multiplex --12 different samples, amplified with unique barcode (3 letter code) - simplified in excel file as individual letters.

'''How to seq RNA:'''
-snakes fed and decapitated upon mouse tail disappearing
-organs removed and flash frozen to preserve RNA
-tissue from organ shaved off to ~.1g to begin RNA isolation kit (2 organs, 6 snakes, 12 total samples)
----may not have sampled right part of organ, may have sampled connective tissue rather than proper organ
-use mRNA to make cDNA copy, cDNA more stable
---used magnetic beads with TTTTTT (poly T) end that binds to AAAAA (poly A), use magnet to attract beads, attached are mRNA
---High throughput sequencing gives only short reads (~75 base pairs from each end)
-fragment mRNA into smaller strands so that with short reads ^^ each bp can be read
-use reverse transcriptase (RT)- enzyme in HIV/retroviruses- to form cDNA from mRNA + nucleotides
-primers made of every possible 6 letter bp combination --> allows us to amplify every primer fragment

-------------------
1/14/16
looking for papers that identify enzymes involved in uptake -- involved in transport proteins across membrane:
- SGLT1 --- glucose transporter

housekeeping genes -- orthologs, python

antiporter - ion travels opposite desired substance

simporter - ion travels across membrane with desired substance

--------------------
1/19/16

antibodies - react based on shape and charge, thus can with substances other than target

--------------------
2/2/16

Reports on individual tasks:

- intestines -> only one notable over-expressed genes "PREDICTED: Python bivittatus solute carrier family 15 (oligopeptide transporter), member 1 (SLC15A1), mRNA" snake 2

-someone found genes linked to gene ontology terms in excel file, will use to annotate genome

-normalizing RNA read data
- > total read-count normalization (TC) divide transcript read count by total number of reads, rescale by factor of counts per million
-> DESeq *** what we are using*** have to normalize both # of reads and length of genes - b/c longer genes (because randomly fragmented) will be overexposed compared to shorter genes

Nick Balanda

2016-02-04T19:28:13Z

Nibalanda:

[[Group 1 intestines]]

[[Notes]]

[[2/4]]

2/4

2016-02-04T19:27:51Z

Nibalanda:

[[Group 1 intestines]]

What are we trying to accomplish?!?!?

1. what is it we want? perfect outcome? How do we get there?
- associate gene oncology with genes from DeSeq.

2. What are we going to do with each of our 12 data sets to evaluate?
-validate sample types - are 6 liver samples comparable liver samples? intestines? use list of housekeeping genes.

-----------------

What triggers cascade?
-transcription activator protein already there? or is g-protein activating transcription protein that turns on keystone transcription factor?
-maybe first look for transcript for transcription factor!!! -> bc this might be the initiator of the cascade

2/4

2016-02-04T19:27:20Z

Nibalanda:

What are we trying to accomplish?!?!?

1. what is it we want? perfect outcome? How do we get there?
- associate gene oncology with genes from DeSeq.

2. What are we going to do with each of our 12 data sets to evaluate?
-validate sample types - are 6 liver samples comparable liver samples? intestines? use list of housekeeping genes. [[Group 1 intestines]]

-----------------

What triggers cascade?
-transcription activator protein already there? or is g-protein activating transcription protein that turns on keystone transcription factor?
-maybe first look for transcript for transcription factor!!! -> bc this might be the initiator of the cascade

2/4

2016-02-04T19:26:52Z

Nibalanda:

What are we trying to accomplish?!?!?

1. what is it we want? perfect outcome? How do we get there?
- associate gene oncology with genes from DeSeq.

2. What are we going to do with each of our 12 data sets to evaluate?
-validate sample types - are 6 liver samples comparable liver samples? intestines? use list of housekeeping genes. [[Group 1 Intestines]]

-----------------

What triggers cascade?
-transcription activator protein already there? or is g-protein activating transcription protein that turns on keystone transcription factor?
-maybe first look for transcript for transcription factor!!! -> bc this might be the initiator of the cascade

2/4

2016-02-04T19:23:23Z

Nibalanda:

What are we trying to accomplish?!?!?

1. what is it we want? perfect outcome? How do we get there?
- associate gene oncology with genes from DeSeq.

2. What are we going to do with each of our 12 data sets to evaluate?

-----------------

What triggers cascade?
-transcription activator protein already there? or is g-protein activating transcription protein that turns on keystone transcription factor?
-maybe first look for transcript for transcription factor!!! -> bc this might be the initiator of the cascade

2/4

2016-02-04T19:23:03Z

Nibalanda:

[[Group 1 Intestines]]
What are we trying to accomplish?!?!?

1. what is it we want? perfect outcome? How do we get there?
- associate gene oncology with genes from DeSeq.

2. What are we going to do with each of our 12 data sets to evaluate?

-----------------

What triggers cascade?
-transcription activator protein already there? or is g-protein activating transcription protein that turns on keystone transcription factor?
-maybe first look for transcript for transcription factor!!! -> bc this might be the initiator of the cascade

2/4

2016-02-04T19:22:28Z

Nibalanda:

What are we trying to accomplish?!?!?

1. what is it we want? perfect outcome? How do we get there?
- associate gene oncology with genes from DeSeq.

2. What are we going to do with each of our 12 data sets to evaluate?

-----------------

What triggers cascade?
-transcription activator protein already there? or is g-protein activating transcription protein that turns on keystone transcription factor?
-maybe first look for transcript for transcription factor!!! -> bc this might be the initiator of the cascade

2/4

2016-02-04T19:12:52Z

Nibalanda: Created page with " What are we trying to accomplish?!?!? 1. what is it we want? perfect outcome? How do we get there? - associate gene oncology with genes from DeSeq. 2. What are we going ..."

Nick Balanda

2016-02-04T18:44:43Z

Nibalanda:

[[Notes]]

[[2/4]]

Notes

2016-02-02T18:52:52Z

Nibalanda:

Snakes 1,2,3 not fed
Snakes 4,5,6 fed
--info in excel file
RNA seq in multiplex --12 different samples, amplified with unique barcode (3 letter code) - simplified in excel file as individual letters.

'''How to seq RNA:'''
-snakes fed and decapitated upon mouse tail disappearing
-organs removed and flash frozen to preserve RNA
-tissue from organ shaved off to ~.1g to begin RNA isolation kit (2 organs, 6 snakes, 12 total samples)
----may not have sampled right part of organ, may have sampled connective tissue rather than proper organ
-use mRNA to make cDNA copy, cDNA more stable
---used magnetic beads with TTTTTT (poly T) end that binds to AAAAA (poly A), use magnet to attract beads, attached are mRNA
---High throughput sequencing gives only short reads (~75 base pairs from each end)
-fragment mRNA into smaller strands so that with short reads ^^ each bp can be read
-use reverse transcriptase (RT)- enzyme in HIV/retroviruses- to form cDNA from mRNA + nucleotides
-primers made of every possible 6 letter bp combination --> allows us to amplify every primer fragment

-------------------
1/14/16
looking for papers that identify enzymes involved in uptake -- involved in transport proteins across membrane:
- SGLT1 --- glucose transporter

housekeeping genes -- orthologs, python

antiporter - ion travels opposite desired substance

simporter - ion travels across membrane with desired substance

--------------------
1/19/16

antibodies - react based on shape and charge, thus can with substances other than target

--------------------
2/2/16

Reports on individual tasks:

- intestines -> only one notable over-expressed genes "PREDICTED: Python bivittatus solute carrier family 15 (oligopeptide transporter), member 1 (SLC15A1), mRNA" snake 2

-someone found genes linked to gene ontology terms in excel file, will use to annotate genome

-normalizing RNA read data
- > total read-count normalization (TC) divide transcript read count by total number of reads, rescale by factor of counts per million
-> DESeq *** what we are using*** have to normalize both # of reads and length of genes - b/c longer genes (because randomly fragmented) will be overexposed compared to shorter genes

Notes

2016-02-02T18:48:41Z

Nibalanda:

Snakes 1,2,3 not fed
Snakes 4,5,6 fed
--info in excel file
RNA seq in multiplex --12 different samples, amplified with unique barcode (3 letter code) - simplified in excel file as individual letters.

'''How to seq RNA:'''
-snakes fed and decapitated upon mouse tail disappearing
-organs removed and flash frozen to preserve RNA
-tissue from organ shaved off to ~.1g to begin RNA isolation kit (2 organs, 6 snakes, 12 total samples)
----may not have sampled right part of organ, may have sampled connective tissue rather than proper organ
-use mRNA to make cDNA copy, cDNA more stable
---used magnetic beads with TTTTTT (poly T) end that binds to AAAAA (poly A), use magnet to attract beads, attached are mRNA
---High throughput sequencing gives only short reads (~75 base pairs from each end)
-fragment mRNA into smaller strands so that with short reads ^^ each bp can be read
-use reverse transcriptase (RT)- enzyme in HIV/retroviruses- to form cDNA from mRNA + nucleotides
-primers made of every possible 6 letter bp combination --> allows us to amplify every primer fragment

-------------------
1/14/16
looking for papers that identify enzymes involved in uptake -- involved in transport proteins across membrane:
- SGLT1 --- glucose transporter

housekeeping genes -- orthologs, python

antiporter - ion travels opposite desired substance

--------------------
2/2/16

Reports on individual tasks:

- intestines -> only one notable over-expressed genes "PREDICTED: Python bivittatus solute carrier family 15 (oligopeptide transporter), member 1 (SLC15A1), mRNA" snake 2

-someone found genes linked to gene ontology terms in excel file, will use to annotate genome

-normalizing RNA read data
- > total read-count normalization (TC) divide transcript read count by total number of reads, rescale by factor of counts per million
-> DESeq *** what we are using***

simporter - ion travels across membrane with desired substance

--------------------
1/19/16

antibodies - react based on shape and charge, thus can with substances other than target

Group 1 intestines

2016-01-26T19:49:03Z

Nibalanda:

Hello

[[Dylan Maghini]]

[[Nick Balanda]]

[[Dustin Atchley]]

[[Housekeeping genes for intestines]]

Analysis Modules:
[http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/]

Notes

2016-01-19T19:54:52Z

Nibalanda:

Housekeeping genes for intestines

2016-01-14T19:41:46Z

Nibalanda:

Hello world!

http://www.ncbi.nlm.nih.gov/nucest/

Orthologs:
villin, fabp2, and cof1 in mammals,
PEPT1 protein --> SLC15A1 gene in humans,
PEPT2 protein --> SLC15A2 gene

Python specific:
http://www.pnas.org/content/110/51/20645.long
Small intestine heat map, figure 1. Investigate further
Genes that undergo large change in expression in small intestine: Pdk4 (mitochondria- increase), Slc35b4 (glycosylation- increase), DOCK7 (development- decrease), PRMT7 (chromatin-increase), DDX21 (transcription- decrease)

Housekeeping genes for intestines

2016-01-14T19:41:30Z

Nibalanda:

Hello world!

http://www.ncbi.nlm.nih.gov/nucest/

Orthologs:
villin, fabp2, and cof1 in mammals
PEPT1 protein --> SLC15A1 gene in humans
PEPT2 protein --> SLC15A2 gene

Python specific:
http://www.pnas.org/content/110/51/20645.long
Small intestine heat map, figure 1. Investigate further
Genes that undergo large change in expression in small intestine: Pdk4 (mitochondria- increase), Slc35b4 (glycosylation- increase), DOCK7 (development- decrease), PRMT7 (chromatin-increase), DDX21 (transcription- decrease)

Notes

2016-01-14T19:21:10Z

Nibalanda:

Notes

2016-01-14T19:15:40Z

Nibalanda:

Notes

2016-01-14T19:10:41Z

Nibalanda:

Notes

2016-01-12T19:55:07Z

Nibalanda: ----

Nick Balanda

2016-01-12T19:22:58Z

Nibalanda:

[[Notes]]

Nick Balanda

2016-01-12T19:20:33Z

Nibalanda: Created page with "."