Saturday, September 24, 2016

biological molecular network types, Carter, Hofree, Ideker 2013 review




cancer network analysis, 2014 Leiserson et al, Nature genetics



2014 Nature genetics
 Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes

Mark D M Leiserson1,2,14, Fabio Vandin1,2,13,14, Hsin-Ta Wu1,2, Jason R Dobson1–3, Jonathan V Eldridge1, Jacob L Thomas1, Alexandra Papoutsaki1, Younhun Kim1, Beifang Niu4, Michael McLellan4, Michael S Lawrence5, Abel Gonzalez-Perez6, David Tamborero6, Yuwei Cheng7, Gregory A Ryslik8, Nuria Lopez-Bigas6,9, Gad Getz5,10, Li Ding4,11,12 & Benjamin J Raphael1,2



"URLs. HI2012 interactome, http://interactome.dfci.harvard.edu/; HotNet2 pan-cancer analysis website, http://compbio.cs.brown.edu/pancancer/hotnet2/; RNA expression data used for the TCGA pan-cancer data set, https://www.synapse.org/#!Synapse:syn1734155; pan-cancer mutations with additional germline variant filtering, https://www.synapse.org/#!Synapse:syn1729383; HotNet2 software release, http://compbio.cs.brown.edu/software." 

Thursday, September 22, 2016

PeerJ line numbers

Line numbers changes on PeerJ PDF even when it is preformated in DOCX. This create a headache when line numbers are cited in response letter. I tried different page margin and format. The lines number still changes between my DOCX and PeerJ pdf.

Tuesday, September 20, 2016

Saturday, September 17, 2016

synthetical lethal interactions


Byte-5:originals hqin$ grep synthe PPI_221205.tab | wc -l
    1062
Byte-5:originals hqin$ pwd

/Users/hqin/data/interaction/mips-yeast/originals

Byte:genetic-interaction hqin$ pwd

/Users/hqin/data/Sce.shanghai/mips/genetic-interaction
Byte:genetic-interaction hqin$ wc -l synthetic.lethals.tab
     441 synthetic.lethals.tab



Friday, September 16, 2016

lab 4


1. The students may have to work to see the Brownian Movement. Don’t let them give up :)

2. It is always a good idea to have the students start section 3 with the potatoes first thing. That way as they are waiting the 45 minutes for the potatoes to sit in the NaCl, they can do the other parts of the lab. After they clean their glassware, have them put the test tubes upside down in the rack with paper towels underneath. It will allow the water to drain before the next class arrives. Things have been going very well on the cleanliness aspect – Thanks! This one is the last really messy lab we have, so keep up the good work.

3. ** Filtration Exercise **
I have posted a modified procedure for the filtration exercise on blackboard. Please let your students know about the new procedure. I basically increased the amount of copper sulfate to use and changed the wood charcoal to some that is powdered (rather than the granular stuff that was there before). This should help things get dissolved and then filter out at the end. Please let me know if you have any questions about this.

4. Paramecium Video: Please refer to my previous email about posting youtube links and the two questions associated with the videos.

5. Feel free to use any of the live specimen for your quizzes next week – I have more if you need to use some. It would be good practice for their lab practical.

Thursday, September 15, 2016

Ubuntu java install



sudo apt-get update

sudo apt-get install default-jre 
 
Also: tried "sudo nano -w /etc/enviroment", but it did not work. 

RNAseq analysis protocol

"Quality of the reads was analyzed with FastQC v0.10.1, and trimming of low-quality reads was performed with Trimmomatic v0.33 [1]. The reads were aligned to the hg19 version of the human genome using the software TopHat v2.0.13 [2]. The Human UCSC hg19 genome annotation was downloaded from Illumina’s FTP repository located on ftp://igenome:G3nom3s4u@ussdftp.
illumina.com/Homo_sapiens/UCSC/hg19/Homo_sapiens_UCSC_hg19.tar.gz. Expression quantification was obtained with CuffLinks v2.2.1, CuffMerge v1.0.0 and CuffDiff v2.2.1. " 

=> FASTQC
http://www.bioinformatics.babraham.ac.uk/projects/download.html


=> Trimmomatic v0.33

=> hg19 from UCSC

=> TopHat

=> Expression quantification CuffLinks v2.2.1, CuffMerge v1.0.0 and CuffDiff v2.2.1.

Wednesday, September 14, 2016

advantages of the 561-nm (Yellow-Green) Laser for the flow cytometry.


a brief list of advantages of the 561-nm (Yellow-Green) Laser for the flow cytometry.

all the RFPs, GFP, CFSE and Fitc when really bright bleed into the PE channel horribly when using just a 488 laser. If you are using the 561 for PE there will be almost no spillover.

—  tdTomato detection (fluorescent protein 283% brighter than eGFP) 
—  MitoTracker Orange (accumulation is dependent upon membrane potential and the dye is well-retained after aldehyde fixation)
— CRISPR-Cas9, an emerging gene-editing (“genome surgery”) tool that frequently uses mCherry reporters. And yellow-green laser is required for optimal excitation of this fluorescent protein
— up to 3.7-fold increase in the stain index with antibodies conjugated with PE and PE based tandem dyes 
— greatly expands the choice of fluorochromes

Sunday, September 11, 2016

UTC BGE course plan

Programming for genome analysis
  Cross-list 4999 in both biology and computer science. 
  Python Bioinformatics programming. 
  Systems biology
  Bioinformatics Programming :: Programming for Genome Analysis.

  Computational biology program at UTC. 
  Human genome analysis. 

Recruit students to help with lab. cross-listing 4999. 

Friday, September 9, 2016

rideside workstation

Ridgeside 
Dell Precision 7810 Desktops. They each had 64GB RAM, 512GB SSD, and a 8 core Xeon chip, as well as a slightly upgraded NVIDIA GPU. These machines ran about $3500 each, a little on the high end, but quite nice. There were also a couple 24 inch monitors that we purchased with each desktop that ran about $350-$450 each;

$lscpu 
CPU(s):                16
On-line CPU(s) list:   0-15
Thread(s) per core:    2
Core(s) per socket:    8
Socket(s):             1
NUMA node(s):          1
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 79
Model name:            Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
Stepping:              1
CPU MHz:               2299.992
CPU max MHz:           3000.0000
CPU min MHz:           1200.0000
BogoMIPS:              4190.30
Virtualization:        VT-x
L1d cache:             32K
L1i cache:             32K
L2 cache:              256K
L3 cache:              20480K
NUMA node0 CPU(s):     0-15
Flags:                 fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi m
mx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopo
logy nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr
pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowpr
efetch epb intel_pt tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpci
d rtm cqm rdseed adx smap xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts

Wednesday, September 7, 2016

NIH postdoc stipend


https://grants.nih.gov/grants/guide/notice-files/NOT-OD-16-131.html


Career Level
Years of Experience
Actual Stipend for FY 2016
Projected Stipend for FY 2017
Monthly Stipend
Postdoctoral
0
$43,692
$47,484
$3,957

1
$45,444
$47,844
$3,987

2
$47,268
$48,216
$4,018

3
$49,152
$50,316
$4,193

4
$51,120
$52,140
$4,345

5
$53,160
$54,228
$4,519

6
$55,296
$56,400
$4,700

7 or More
$57,504
$58,560

Qbio related matrials


http://ecotheory.biology.gatech.edu/courses/foundations-in-quantitative-biosciences

http://ecotheory.biology.gatech.edu/sites/default/files/syllabus_biol_8804_f16.pdf

NGS

Cancer genomics


Evolve manual pipettes


I wanted to take a moment to remind you of our newest product offering and current promotions.  Our new Evolve manual pipettes provide an innovative way of changing volumes which will speed up daily pipetting.  Instead of changing volumes by twisting the plunger, which can take a long time going from one end of the range to the other, our Evolve pipettes have three dials so you can change each digit independently of each other.  This new technique will reduce the amount of time by more than 50% compared to your current manual pipettes.  They are extremely ergonomic and light and come with two different plunger springs to customize the feel that you want.  These pipettes still come with the Integra Grip Tip interface which ensures a supreme tip attachment without banging them on while also guaranteeing zero issues with tips falling off or leaking.  We offer single and multichannel models with a range of 0.2ul-5000ul across many different pipettes.

In conjunction with this launch, we are running two promotions with very aggressive pricing that run for the rest of the year.  The first is a trade in promotion.  You can trade in any pipette in any condition for a huge discount.  The second promotion is for a starter pack where you get the 20, 200, and 100ul single channels, 10ml reservoir sample pack, 3 cases of tips, and 3 shelf hooks.

I understand it is a lot to ask for you to convert your pipettes and tips; so I am willing to discuss the possibility of “buying back” some of your current tip stock.  With this new aggressive pipette pricing and huge tip offer, I know you will not find a better value.  I look forward to working with you.

Evolve manual pipettes



cid:image001.jpg@01CFE3CA.572D28B0
2 Wentworth Dr.
Hudson, NH 03051

Banner_Q2_EVOLVE_Trade_in_US_V02

Banner_Q2_EVOLVE_Starter_pack_US



Tuesday, September 6, 2016

UTC faculty evaluation form, EDO

UTC faculty evaluation EDO form
http://www.utc.edu/academic-affairs/pdfs/1-provost-page-forms/fac-eval-form-rev4-2016.pdf


Monday, September 5, 2016

The BD2K Guide to the Fundamentals of Data Science, a series of online lectures



The NIH Big Data to Knowledge program is pleased to announce The BD2K
Guide to the Fundamentals of Data Science, a series of online lectures
given by experts from across the country covering a range of diverse
topics in data science.  This course is an introductory overview that
assumes no prior knowledge or understanding of data science.

The series starts Friday, September 9th and will run all year once per
week at 12noon-1pm ET.


  *** To join the meeting online:
https://global.gotomeeting.com/join/786506213
  *** To join by phone only: +1 (872) 240-3311; Access Code: 786-506-213
  *** First GoToMeeting? Try a test session:
http://help.citrix.com/getready


This is a joint effort of the BD2K Training Coordinating Center (TCC), the
BD2K Centers Coordination Center (BD2KCCC), and the NIH Office of the
Associate Director of Data Science.  For up-to-date information about the
series and to see archived presentations, go to:
http://www.bigdatau.org/data-science-seminars.  Below is a tentative
schedule.




SCHEDULE
9/9/16          Introduction to big data and the data lifecycle (Mark Musen,
Stanford)

9/16/16         SECTION 1: DATA MANAGEMENT OVERVIEW (Bill Hersh, Oregon Health
Sciences)
9/23/16         Finding and accessing datasets, Indexing  and Identifiers (Lucila
Ohno-Machado, UCSD)
9/30/16         Data curation and Version control (Pascale Gaudet, Swiss
Institute of Bioinformatics)
10/7/16         Ontologies (Michel Dumontier, Stanford)
10/14/16        Metadata standards (Zachary Ives, Penn)
10/21/16        Provenance (Suzanne Sansone, Oxford)

10/28/16        SECTION 2: DATA REPRESENTATION OVERVIEW  (Anita Bandrowski, UCSD)
11/4/16         Databases and data warehouses, Data: structures, types,
integrations (Chaitan Baru, NSF)
11/11/16        No lecture ‹ Veteran¹s Day
11/18/16        Social networking data (TBD)
12/2/16         Data wrangling, normalization, preprocessing (Joseph Picone,
Temple)
12/9/16         Exploratory Data Analysis (Brian Caffo, Johns Hopkins)
12/16/16        Natural Language Processing (Noemie Elhadad, Columbia)


1/6/17          SECTION 3: COMPUTING OVERVIEW (Dates tentative)
1/13/17         Workflows/pipelines
1/20/17         Programming and software engineering; API; optimization
1/27/17         Cloud, Parallel, Distributed Computing, and HPC
2/3/17          Commons: lessons learned, current state

2/10/17         SECTION 4: DATA MODELING AND INFERENCE OVERVIEW (Dates tentative)
2/17/17         Smoothing, Unsupervised Learning/Clustering/Density Estimation
2/24/17         Supervised Learning/prediction/ML, dimensionality reduction
3/3/17          Algorithms, incl. Optimization
3/10/17         Multiple testing, False Discovery rate
3/17/17         Data issues: Bias, Confounding, and Missing data
3/24/17         Causal inference
3/31/17         Data Visualization tools and communication
4/7/17          Modeling Synthesis

                SECTION 5: ADDITIONAL TOPICS
4/14/17         Open science
4/21/17         Data sharing (including social obstacles)
4/28/17         Ethical Issues
5/5/17          Extra considerations/limitations for clinical data
5/12/17         reproducibility
5/19/17         SUMMARY and NIH context


Reasonable accommodation: Individuals with disabilities who need
reasonable accommodation to participate in this event should contact
Kristan Brown or Sonyka Ngosso at 301-402-9827. Requests should be made at
least 5 business days in advance of the event.  For questions, contact
Crystal Stewart (crystal.stewart@loni.usc.edu).


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