GeAge http://senescence.info/

http://senescence.info/

de Magalhaes JP, Toussaint O. GenAge: a genomic and proteomic

network map of human ageing. FEBS Lett 2004;571:243–7

http://genomics.senescence.info/genes/

The site provide downloadable data.

There are also drug related data. 

enet

> enet
function (x, y, lambda = 0, max.steps, normalize = TRUE, intercept = TRUE, 
    trace = FALSE, eps = .Machine$double.eps) 
{
    call <- match.call()
    nm <- dim(x)
    n <- nm[1]
    m <- nm[2]
    im <- seq(m)
    one <- rep(1, n)
    vn <- dimnames(x)[[2]]
    meanx <- drop(one %*% x)/n
    if (intercept == FALSE) {
        meanx <- rep(0, m)
    }
    x <- scale(x, meanx, FALSE)
    normx <- sqrt(drop(one %*% (x^2)))
    if (normalize == FALSE) {
        normx <- rep(1, m)
    }
    if (any(normx < eps * sqrt(n))) 
        stop("Some of the columns of x have zero variance")
    names(normx) <- NULL
    x <- scale(x, FALSE, normx)
    mu <- mean(y)
    if (intercept == FALSE) {
        mu <- 0
    }
    y <- drop(y - mu)
    d1 <- sqrt(lambda)
    d2 <- 1/sqrt(1 + lambda)
    Cvec <- drop(t(y) %*% x) * d2
    ssy <- sum(y^2)
    residuals <- c(y, rep(0, m))
    if (lambda > 0) {
        maxvars <- m
    }
    if (lambda == 0) {
        maxvars <- min(m, n - 1)
    }
    if (missing(max.steps)) {
        max.steps <- 50 * maxvars
    }
    L1norm <- 0
    penalty <- max(abs(Cvec))
    beta <- rep(0, m)
    betactive <- list(NULL)
    first.in <- integer(m)
    active <- NULL
    Actset <- list(NULL)
    df <- 0
    if (lambda != 0) {
        Cp <- ssy
    }
    ignores <- NULL
    actions <- as.list(seq(max.steps))
    drops <- FALSE
    Sign <- NULL
    R <- NULL
    k <- 0
    while ((k < max.steps) & (length(active) < maxvars)) {
        action <- NULL
        k <- k + 1
        inactive <- if (k == 1) 
            im
        else im[-c(active, ignores)]
        C <- Cvec[inactive]
        Cmax <- max(abs(C))
        if (!any(drops)) {
            new <- abs(C) == Cmax
            C <- C[!new]
            new <- inactive[new]
            for (inew in new) {
                R <- updateRR(x[, inew], R, x[, active], lambda)
                if (attr(R, "rank") == length(active)) {
                  nR <- seq(length(active))
                  R <- R[nR, nR, drop = FALSE]
                  attr(R, "rank") <- length(active)
                  ignores <- c(ignores, inew)
                  action <- c(action, -inew)
                  if (trace) 
                    cat("LARS-EN Step", k, ":\t Variable", inew, 
                      "\tcollinear; dropped for good\n")
                }
                else {
                  if (first.in[inew] == 0) 
                    first.in[inew] <- k
                  active <- c(active, inew)
                  Sign <- c(Sign, sign(Cvec[inew]))
                  action <- c(action, inew)
                  if (trace) 
                    cat("LARS-EN Step", k, ":\t Variable", inew, 
                      "\tadded\n")
                }
            }
        }
        else action <- -dropid
        Gi1 <- backsolve(R, backsolvet(R, Sign))
        A <- 1/sqrt(sum(Gi1 * Sign))
        w <- A * Gi1
        u1 <- drop(x[, active, drop = FALSE] %*% w * d2)
        u2 <- rep(0, m)
        u2[active] <- d1 * d2 * w
        u <- c(u1, u2)
        if (lambda > 0) {
            maxvars <- m - length(ignores)
        }
        if (lambda == 0) {
            maxvars <- min(m - length(ignores), n - 1)
        }
        if (length(active) >= maxvars) {
            gamhat <- Cmax/A
        }
        else {
            a <- (drop(u1 %*% x[, -c(active, ignores)]) + d1 * 
                u2[-c(active, ignores)]) * d2
            gam <- c((Cmax - C)/(A - a), (Cmax + C)/(A + a))
            gamhat <- min(gam[gam > eps], Cmax/A)
            Cdrop <- c(C - gamhat * a, -C + gamhat * a) - (Cmax - 
                gamhat * A)
        }
        dropid <- NULL
        b1 <- beta[active]
        z1 <- -b1/w
        zmin <- min(z1[z1 > eps], gamhat)
        if (zmin < gamhat) {
            gamhat <- zmin
            drops <- z1 == zmin
        }
        else drops <- FALSE
        beta[active] <- beta[active] + gamhat * w
        betactive[[k]] <- beta[active]
        Actset[[k]] <- active
        residuals <- residuals - (gamhat * u)
        Cvec <- (drop(t(residuals[1:n]) %*% x) + d1 * residuals[-(1:n)]) * 
            d2
        L1norm <- c(L1norm, sum(abs(beta[active]))/d2)
        penalty <- c(penalty, penalty[k] - abs(gamhat * A))
        if (any(drops)) {
            dropid <- seq(drops)[drops]
            for (id in rev(dropid)) {
                if (trace) 
                  cat("LARS-EN Step", k, ":\t Variable", active[id], 
                    "\tdropped\n")
                R <- downdateR(R, id)
            }
            dropid <- active[drops]
            beta[dropid] <- 0
            active <- active[!drops]
            Sign <- Sign[!drops]
        }
        if (!is.null(vn)) 
            names(action) <- vn[abs(action)]
        actions[[k]] <- action
    }
    allset <- Actset[[1]]
    for (i in 2:k) {
        allset <- union(allset, Actset[[i]])
    }
    allset <- sort(allset)
    max.p <- length(allset)
    beta.pure <- matrix(0, k + 1, max.p)
    for (i in 2:(k + 1)) {
        for (j in 1:length(Actset[[i - 1]])) {
            l <- c(1:max.p)[allset == Actset[[i - 1]][j]]
            beta.pure[i, l] <- betactive[[i - 1]][j]
        }
    }
    beta.pure <- beta.pure/d2
    dimnames(beta.pure) <- list(paste(0:k), vn[allset])
    k <- dim(beta.pure)[1]
    df <- 1:k
    for (i in 1:k) {
        a <- drop(beta.pure[i, ])
        df[i] <- 1 + length(a[a != 0])
    }
    residuals <- y - x[, allset, drop = FALSE] %*% t(beta.pure)
    beta.pure <- scale(beta.pure, FALSE, normx[allset])
    RSS <- apply(residuals^2, 2, sum)
    R2 <- 1 - RSS/RSS[1]
    Cp <- ((n - m - 1) * RSS)/rev(RSS)[1] - n + 2 * df
    object <- list(call = call, actions = actions[seq(k)], allset = allset, 
        beta.pure = beta.pure, vn = vn, mu = mu, normx = normx[allset], 
        meanx = meanx[allset], lambda = lambda, L1norm = L1norm, 
        penalty = penalty * 2/d2, df = df, Cp = Cp, sigma2 = rev(RSS)[1]/(n - 
            m - 1))
    class(object) <- "enet"
    object
}
<environment: namespace:elasticnet>

*** essential genes in human, cancer cell lines

http://cancerdiscovery.aacrjournals.org/content/2/2/172/suppl/DC1


https://figshare.com/collections/Parallel_genome_scale_loss_of_function_screens_in_216_cancer_cell_lines_for_the_identification_of_context_specific_genetic_dependencies/1019859

UT genomics facility

http://mbrf.utk.edu/

Millipore Guava Easy Cyte 6HT-2L Flow Cytometer

UTC CS Master and PhD curriculum

http://catalog.utc.edu/preview_program.php?catoid=15&poid=2275&returnto=473

Computer Science Core Courses

---

• CPSC 5100 - Theory of Computer Programming Languages
• CPSC 5210 - Design and Analysis of Computer Algorithms
• CPSC 5550 - Client-Server Systems
• CPSC 5700 - Advanced Computer Architecture
• CPSC 5800 - Advanced Topics in Systems Software
• Total: 15 hours
• 
  

UTCComputer Science and Engineering Undergraduate Curriculum

Computer Science and Engineering Undergraduate Curriculum

http://www.utc.edu/college-engineering-computer-science/programs/computer-science-engineering/undergradprograms.php

http://catalog.utc.edu/preview_program.php?catoid=14&poid=2046&returnto=432


http://www.utc.edu/advisement/pdfs/clear-paths/clearpaths-2015-2016/cecs-2015/cecs-cpsc-scientificapplications-bs2015.pdf

Editing Genomes to Record Cellular Histories

Whole organism lineage tracing by combinatorial and cumulative genome editing

http://science.sciencemag.org/content/early/2016/05/25/science.aaf7907


http://www.the-scientist.com/?articles.view/articleNo/46173/title/Editing-Genomes-to-Record-Cellular-Histories/&utm_campaign=NEWSLETTER_TS_The-Scientist-Daily_2016&utm_source=hs_email&utm_medium=email&utm_content=30023521&_hsenc=p2ANqtz-_R1n6GFzmrTt34_hkUqUVfwahxXsR3FIUKRDnqbJEghIVaQfXQ6FAd2sYy_HyvmbyFGoI2o9ihE5P2eTgED-mCTiOXyA&_hsmi=30023521

CS + premed, biology

CS + premed, biology

What is pre-med
https://en.wikipedia.org/wiki/Pre-medical


Vanderbilt
http://engineering.vanderbilt.edu/eecs/Undergraduate/PremedicalTrackCS.php

Northwestern, Computer science and biology. It is 5 year program, double-major?
https://www.northeastern.edu/registrar/1112-cram-123.pdf

toread, Effects of reciprocity on random walks in weighted networks Zhang, Li, Scientific Report

Effects of reciprocity on random walks in weighted networks
Zhang, Li, Scientific Reports

(toread) CRISPR mapping of QTL

http://science.sciencemag.org/content/early/2016/05/04/science.aaf5124

Glenn foundations

Glenn foundation for medical research

http://glennfoundation.org/

YODA

requires MYSQL and PHP. This can be cumbersome.  A standalone package would be much better.

https://code.google.com/archive/p/sageweb-yoda/

MET15, rDNA on yeast Chromosome XII (12)

rDNA locus

Chromosome XII 458991..459097

http://www.yeastgenome.org/locus/S000120636/overview

According to Saka 2015 NAR, rDNA occupies 60% of chromosome XII. 

See:  Yeast rDNA Stability DataBase, YRSD at http://lafula-com.info/kobayashiken/geldata/index.php.

Met17/Met15 locus

Chromosome XII 732542..733876

http://www.yeastgenome.org/locus/S000004294/overview

notes: a budding yeast's perspective on aging: the shape I'm in

Minireview, by J Smith, J Wright, B Schneider, 2015

SWL15 claims that DR increase cell cycle time and decrease cell size. SWL15 argues for a link between cell cycle time, cell size and lifespan. 

DR sometimes shortens lifespan and sometime extends lifespan. 

de Magalhaes JP, Toussaint O. GenAge: a genomic and proteomic

network map of human ageing. FEBS Lett 2004;571:243–7

Yeast Resource Center at U W Seattle

Provided genomes, mass spec, RNA seq data. Description of the experimental conditions are not clear.

http://www.yeastrc.org/g2p/about.do

Funded by P41 GM103533

baidu cloud client

baidu cloud client eat's up 31.99G RAM on my mactower.

Feltus et al 2015, genomic big data transfer, a practical guide

 The Widening Gulf between Genomics Data Generation and Consumption: A Practical Guide to Big Data Transfer Technology

 Frank A. Feltus1, Joseph R. Breen III2, Juan Deng3, Ryan S. Izard3, Christopher A. Konger4, Walter B. Ligon III3, Don Preuss5 and Kuang-Ching Wang3

 Supplementary Issue: Current Developments in RNA Sequence Analysis

4 key areas: 

1) data transfer networks, protocols, and applications; 

2) data transfer security including encryption, access, firewalls, and the Science DMZ; 

3) data flow control with software-defined networking; 

4) data storage, staging, archiving and access. 

key concepts in systems biology

Key concepts in undergraduate systems biology educaiton

Oscillation

Bistability (bifurcation)

GRE percentiles, TOEFL percentiles

https://www.ets.org/gre/revised_general/about

Verbal, quantitative, and analytical writing

From
http://www.ets.org/s/gre/pdf/gre_guide.pdf

GRE scores:  Average in 2020 is verbal 150, quant 154. So 314 seem to be just above average , 165 in quant (90%), verbal 149 (37%) in 2020.  

https://www.ets.org/s/toefl/pdf/94227_unlweb.pdf

So, Reading 30, good,  Listening 23 (OK) Speaking 17 (below average) Writing 25 (good)

IETST score

For UTC, it requires TOEFL > 550, IELTS > 6.0 

http://catalog.utc.edu/content.php?catoid=11&navoid=243

Lenski's E coli experimental data sets

E coli evolution

http://myxo.css.msu.edu/ecoli/genomicsdat.html

Spelman grade change

http://www.spelman.edu/docs/admissions/change_of_grades_submission.pdf?sfvrsn=0

bio125 final exam

Paper exam and scantron as backups
Make sure there are enough ethernet cables in the room.
Ethernet cables
Random seatings
Backup calculators
Lockdown Browwer need to be installed on department computer every time.

Sec4, performance was poor. I was surprised that few students did the bonus point quiz that was designed to help student prepared for the final exam.

Sec4. I made one set of bonus quiz and one set of practice quiz. This time more students seem to take the quizzes. One student apparently studied hard but did poorly in the closed book exam.
The strongest and weakest students finished the exam quickly.


Reference:
http://hongqinlab.blogspot.com/2016/03/bio125-miderm-exam.html