How To Get System Info In Linux

http://www.computerbob.com/guests/how_to_get_system_info_in_linux.php

Here's a list of commands, tips and info that I've compiled from several different sources. I use this as a quick referencewhen I need to find info about my computer or my operating system.

General System Info

Note: Most hardware info commands should be run as root. Some even show more info when run as root.

Run qtparted or GParted for hard drive and partition info!!!
Also, see KDiskFree. And fdisk -l for info on all partitions.

hardinfo - good report to run; also, check the Details tab.

Ko's Method For Getting A Hardware Report:
This report runs a series of reports, some of which are listed individually further on in this document.
Install through Synaptic the package installation-report.
Then run in a maximized konsole:
report-hw
or
report-hw > hwreport (Sends the output to a textfile called hwreport in your home folder)

sysinfo a nice graphical display, run it from a terminal.

hal-device-manager
kde-hal-device-manager
These packages can be installed from Synaptic. Use kde-hal-device-manager by clicking KMenu > System > DeviceManager.

dmidecode -t memory (as root) The dmidecode tool dumps your system’s DMI (Desktop Management Interface) table contents in a human-readable format. This table contains information regarding system’s hardware components, as well as BIOS revisions etc. dmidecode output not only describes system current configuration, but also reports the BIOS limitations on supported CPU speed, Maximum Memory allowed and more.
dmidecode | less (as root) BIOS info and system info (space to page down, q to quit)

uname -a (info on your server)
uname -m machine number (i.e. - i686)
uname -r kernel version

lshw (as root) - list hardware; might want to run lshw | less (space to page-down, q to quit)
lshw-gtk (as root) - download this from Synaptic; it can be started from the terminal; click on any item for info; double click on the the items in bold to drill down; excellent front-end for lshw.

lsb_release -a (release info) (don't forget the underscore) (or see /etc/lsb_release)
***In Mepis Linux 6.5, this command returns:
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 6.06.1 LTS
Release: 6.06
Codename: dapper

In Mepis 6.5, one solution for finding the version that you're running is to go to take a look at the login screen.

lspci (as root) (controllers, etc.) (VERY good report; -v is verbose, -vv is very verbose
lspci -tv (as root) shows tree
lsusb, lsusb -tv - list usb devices
lsmod (at root, shows kernel modules currently loaded. Also try lsmod | less)

/proc - there's important info located in this directory! cd /proc, then ls; use cat to read the files in /proc such ascpuinfo, devices, filesystems, meminfo, partitions, swaps, uptime, version, etc.; also, you can cd into the subdirectoriesin /proc, for example cd driver.
Examples:
cat /proc/cpuinfo (or cat cpuinfo from inside /proc)
cat /proc/version (or cat verson from inside /proc) - version info
cat /proc/swaps (or cat swaps from inside /proc) will show info on each swap partition.

top - display Linux tasks, real-time; system summary info and a list of tasks currently being managed by the kernel - includes a live report on swap being used; type Shift-f to get sort options while running (default sort is k - CPU usage)

htop - even better than top; interactive process viewer can be used to kill processes.

ctrl-esc (from anywhere in KDE) - brings up the ProcessTable

procinfo – procinfo is a small program that gathers some system information from diverse files under /proc and prints it to the screen. It duplicates some of the functionality of free(1) and uptime(1). This also includes lsdev for displaying information about installed hardware and socklist for displaying a list of open sockets.

Other Ways To List Processes:
ps aux
ps -e

swapon -s shows swap partitions and info
swapon -a makes all swap partitions available as indicated in /etc/fstab.

fdisk -l gives all partition info on hard disk(s).

/etc/fstab (the fstab file contains file system configuration information)

free (memory usage, in kilobytes.
free -m (memory usage, in MB).

ksysguard application and ksysguard tray applet both show memory usage

df -h (disk info about all mounted file systems, human-readable)
df -hT (disk info about all mounted file systems, human-readable, showing file system type)

du / -bh | more detailed diskusage for each subdirectory starting at root, human readable; use space to page down, q to quit.
du -s /var/log/* - displays space usage of all the files in /var/log

hdparm -t /dev/hda (as root) - display disk performance of hda

This shows what the cpu identifies itself as:
arch (but it could be wrong, see command below)
rpm --showrc| grep " arch" (if rpm is installed)

ifconfig -a – displays IP address and other info

Time Info Commands:
date
uptime

---

Package Info

apt-show-versions > InstalledPkgs (as root) - outputs the text file InstalledPkgs to your home directory. This shows all packages on your system. This list won't be alphabetized. You can copy and paste it into a spreadsheet and then sort it. You can download apt-show-versions with Synaptic. See man apt-show-versions for more info. Another option is to look at the installed packages in Synaptic.

ls /var/cache/apt/archives > AddedPkgs outputs the text file AddedPkgs to your home directory. This shows all of the packages that you've added to your system (unless you've deleted this info, or unless Synaptic has been set to delete it!). There's also a "partial" directory, so
ls /var/cache/apt/archives/partial > PartialPkgs to output a text file called PartialPkgs.

To get a package list sorted by size:
dpkg-query -W --showformat='${Installed-Size} ${Package}\n' | sort -n

---

Here's a section that I tacked on, just so I'd have a quick reference to some helpful bash commands.

Bash Info

echo $PATH show the content of the environmental variable named PATH
echo $? shows the exit code of the last command

help shows a list of shell builtin commands, and help name shows help on the command name; also, see the section on Shell Builtin Commands in man bash

whereis locates the binary, source, and manual page files for a command.

which returns the pathnames of the files which would be executed in the current environment, had its arguments been given as commands in a strictly POSIX-conformant shell. It does this by searching the PATH for executable files matching the names of the arguments. Note: In Mepis Linux, which is an alias for type -path. Although that command is not explicitly defined, try the command help type for more info on the type command. which=type -path may be the same as type -P. In practice, the which command and the type -path command may be equivalent. To see the truewhich command in action in Mepis Linux, type "which" (use quotes) (or unalias which to permanently remove the alias).

type (name) indicates how it would be interpreted if used as a command name

alias shows a list of all aliases
unalias will remove an alias permanently
"command" quoting the command will run the "true" command (if it exists) instead of the alias

file guesses what type of file
file file
file -i file for more info
file -iz file for compressed files
file -s file for block or special character files

tree – list directory structure and content in a tree-like format; by default lists the contents of the directory that you're in.

cal - current month
cal -3 - last month, current month, and next month
cal [month] year - specify month, or month and year [examples: cal august 2007; cal 2006]

Structural Genomics: Correlation LD Blocks, Population Structure, and Genome Architecture

Structural Genomics: Correlation Blocks, Population Structure, and GenomeArchitecture

GeneProf - a web based tools for Next Generation data analysis

http://www.geneprof.org/GeneProf/

Homoploid hybrid expectations

What is your expectation for a homoploid hybrid species?

1. Genetic and phenotypic divergence of homoploid hybridspecies from parental species
http://www.nature.com/hdy/journal/v108/n3/full/hdy201180a.html

2. Molecular genetic and quantitative trait divergenceassociated with recent homoploid hybrid speciation: astudy of Senecio squalidus (Asteraceae)
http://www.nature.com/hdy/journal/v108/n2/full/hdy201146a.html

an understanding guide on mixed effects models

http://www2.hawaii.edu/~kdrager/MixedEffectsModels.pdf

population genomics of maize

1. Comparative population genomics of maize domestication and improvement

2. Genome-wide genetic changes during modern breeding of maize

3. Dynamic evolution of bz orthologous regions in the Andropogoneae and other grasses
We estimate that the  average divergence times between maize and Tripsacum, Coix and Sorghum are 8.5,  12.1, and 12.4 million years ago (MYA), respectively, and that between Coix and Sorghum is 9.3 MYA.

4. data set
http://www.panzea.org/lit/data_sets.html#HapMapV2

5. annotation
ftp://ftp.jgi-psf.org/pub/JGI_data/phytozome/v8.0/Zmays/annotation/

6.  Sireviruses represent theonly TE genus whose members contain a variety of highly conserved short (20-40bp) sequencemotifs, regardless of the evolutionary distance of their hosts. 
http://bat.ina.certh.gr/research/TEs/index.html


7. repeat annotation for maize genome
http://ftp.maizesequence.org/current/repeats/
readme for all maize genomic data
http://ftp.maizesequence.org/current/README.txt

http://archive.maizesequence.org/index.html

mips Repeat Element database (mips-REdat)
http://mips.helmholtz-muenchen.de/plant/cgenomics.jsp


8. domestication of maize
http://teosinte.wisc.edu/questions.html

Earliest Evidence Of Domesticated Maize Discovered: Dates Back8,700 Years

classical frequentist and Bayesians are not the only schools of statistics useful for animal models

http://www.quantumforest.com/2011/11/coming-out-of-the-bayesian-closet/#comments


I would also like to point out that classical frequentist and Bayesians are not theonly schools of statistics useful for animal models.

Classical frequentist inference uses marginal likelihood (random effects integratedout) that includes fixed parameters and only the observations are treated asrandom.
Bayesian inference is a probabilistic framework that combines likelihood and prior information, and treats all parameters and observations as random.
A third important school is the one based on the Extended Likelihood Principle. Assuming simple statistical principles Bjørnstad (1996) showed that all information in the data about the random and fixed effects is included in a joint likelihood including three components: fixed parameters, unobserved random effects, and observations as random. Lee and Nelder's (1996) h-likelihood is an implementation of the Extended Likelihood Principle. The hglm package (that I have developed together with colleagues) is based on h-likelihood theory and allows fitting of animal models.

hglm: Hierarchical Generalized Linear Models

Evidence for a Common Toolbox Based on Necrotrophy in a Fungal Lineage

blog of esa

http://www.esa.org/esablog/

Exploring Massive, Genome Scale Datasets with the GenometriCorr Package

Population genomics study on phage

Population genomics and phylogeography of an Australian dairy factory derived lytic bacteriophage

野田黄雀行

野田黄雀行（曹植）


高树多悲风，海水扬其波。
利剑不在掌，结友何须多？
不见篱间雀，见鹞自投罗？
罗家得雀喜，少年见雀悲。
拔剑捎罗网，黄雀得飞飞。
飞飞摩苍天，来下谢少年。

Shared spatial effects on quantitative genetic parameters

http://onlinelibrary.wiley.com/doi/10.1111/j.1558-5646.2012.01620.x/full

Linear mixed-effects models (LMMs) were conducted in ASReml3 (VSN International, Hemel Hempsted, UK; Gilmore et al. 2009). We used two techniques to incorporate spatial information into the models. First, we fitted average lifetime spatial coordinates as ordered row and column effects, fitted as additional random effects, with a covariance structure that assumed a first-order separable autoregressive processto account for spatial dependence (AR1 × AR1, Gilmour et al. 1997). Second, we incorporated information on home range overlap between individuals into the animal model by fitting a vector of shared home range effects as an additional random effect, with the corresponding covariance matrix , where S is the home range overlap matrix (the “S matrix”). For full details of how we incorporated spatial information into linear mixed models, please see File 2 in supporting information.

Repeatability for Gaussian and non-Gaussian data - guide

http://onlinelibrary.wiley.com/doi/10.1111/j.1469-185X.2010.00141.x/full

see R package, rptR

take care of the heteroscedasticity of erros in your study

The assumption of homoscedasticity is made for statistical reasons rather than biological reasons; in most real datasets, some form of heteroscedasticity is likely to exist.


http://www.springerlink.com/content/7111wxg10j657q04/

How does a Bioinformatics Scientist document his/her work

This a question raised in BioStar, please go there:
http://www.biostars.org/post/show/46444/how-does-a-bioinformatics-scientist-document-hisher-work/#46514

Natural polymorphisms suppress (buffer) effects of new mutations

 The predominant directionof epistatic effects was to suppress the mutant phenotype. These observations support a previous studyon startle behaviour using the same D. melanogaster chromosome substitution lines, which concluded that suppressing epistasis may buffer the effects of new mutations. However, epistatic effects are notcorrelated among the different phenotypes. Thus, suppressing epistasis appears to be a pervasive general feature of natural populations to protect against the effects of new mutations, but different epistatic interactions modulate different phenotypes affected by mutations at the same pleiotropic gene.

ggplot2 - analyzing gene expression data

Differential gene and transcript expression analysis ofRNA-seq experiments with TopHat and Cufflinks

names for specific interglacials

http://en.wikipedia.org/wiki/File:Atmospheric_CO2_with_glaciers_cycles.gif

Peter's R Programming Pages

Scatter and pairs plots using the "Iris" data

Simple linear regressions and Linear Models using the "Iris" data

Drawing network graphs ofnodes and edges

Filled contour plots & level plots for matrix data with row and column names

Ramachandran Plots, using filled contour plots with an overlayed scatter plot

Loading GEO microarray data (GEO SOFT files)

Drawing heatmaps for microarrays

Drawing 3D bar charts and histograms with OpenGL using RGL

Combining the R and Python programming languages

http://www2.warwick.ac.uk/fac/sci/moac/people/students/peter_cock/r/

ASReml-R cookbook

http://apiolaza.net/asreml-r/


Recipes

1. Very basic usage.
2. Specifying model equations.
3. Univariate analysis, including basic equivalent models, diallels, clonal trials and multiple-site as single trait.
4. Extracting results like variance components, fixed and random effects, etc, from the fitted model.
5. Covariance structures.
6. Multiple environments.

Forest plots using R and ggplot2

http://statbandit.wordpress.com/2010/11/01/forest-plots-using-r-and-ggplot2/

for loop to sapply loop

http://thr3ads.net/r-help/2012/01/1830182-converting-a-for-loop-into-a-foreach-loop

You may not need foreach if you use the power of R and vectorize your
operations.  Instead of going through the loops and extracting subsets
at each iteration, use 'split' to either split the dataframe into the
subsets, or faster, create a set of indices to access the subsets of
data:

Here is one way of doing it; create a list of indices to split up the
data instead of doing it with 'for' loop and extracting dataframes at
each point (very time consuming).

> n <- 10000
> x <- data.frame(ID = sample(1:10, n, TRUE)
+               , day = sample(1:7, n, TRUE)
+               , hour1 = sample(0:23, n, TRUE)
+               , value = runif(n)
+               )

> # create list of indices to split the data
> idh <- split(seq(nrow(x)), list(x$ID, x$day, x$hour1), drop = TRUE)
> str(idh)  # sample of the indices
List of 1675
 $ 1.1.0  : int [1:5] 226 795 869 6617 9496
 $ 2.1.0  : int [1:11] 479 3483 3702 4660 4876 5373 5479 5960 6580 6956 ...
 $ 3.1.0  : int [1:9] 383 668 2437 3877 5290 5835 7003 7896 8905
 $ 4.1.0  : int [1:3] 1493 3502 9635
 $ 5.1.0  : int [1:2] 2480 6237
 $ 6.1.0  : int [1:5] 2061 4898 5288 9439 9692

> # now take the means of each set of indices
> imeans <- sapply(idh, function(i) mean(x$value[i]))
> head(imeans, 20)
    1.1.0     2.1.0     3.1.0     4.1.0     5.1.0     6.1.0     7.1.0
   8.1.0     9.1.0    10.1.0     1.2.0
0.6231298 0.2556291 0.4942764 0.5416091 0.9509064 0.4968711 0.4037645
0.4107976 0.4189220 0.5922433 0.5581944
    2.2.0     3.2.0     4.2.0     5.2.0     6.2.0     7.2.0     8.2.0
   9.2.0    10.2.0
0.6275555 0.6411061 0.4885817 0.5413741 0.4134971 0.4838082 0.5207435
0.4018991 0.5338913

#####

If 'idh' is the set of indices resulting from the 'split', then
your
function would probably have to look like this:

myfun <- function(x){
H.scv <- Hscv(data[x, ], pilot = "unconstr")
KDE <- kde(data[x, ], H=H.scv, approx.cont=TRUE)
}

This is based on your original example where 'data' was the object
name of your data.  You want to use the indices to access the relavent
rows in your data.  Not sure what your function is supposed to be
returning; right now it return the last value which would be the
result of the call to 'kde'.  If you want both results returned, then
the last statement should possibly be:

list(H.scv, KDE)

which will return the values in a list that you can then extract from.

parallel in linux

Use ‘parallel’ for easy multi-processor execution

I just discovered the parallel utility, which is a easy way to make use of multiple processors while munging data from the shell. I especially like that I can pipe data directly in and out of it, just like the other shell utils (sed, awk, cut, sort, etc).

From the examples on that page:
Use imagemagick’s “convert” command to downsize many images to thumbnails:
ls *.jpg | parallel -j +0 convert -geometry 120 {} thumb_{}

“-j +0″ means use as many processes as possible, based on the number present in the system

Or do it recursively using find:
find . -name '*.jpg' | parallel -j +0 convert -geometry 120 {} {}_thumb.jpg

I suspect this will become an integral part of my pipelines soon.

Training manual on spatial analysis of plant diversity and distribution

http://www.bioversityinternational.org/index.php?id=19&user_bioversitypublications_pi1[showUid]=5052

A tutorial for DivaGIS.

tomato genome sequenced

The tomato genome sequence provides insights into fleshy fruit evolution

significant additive detected

We also detected significant additive genetic variance for the four morphological traits tested: adult weight, leg length, horn length, and scrotal circumference. The other fitted random effects were statistically significant sources of variance for all traits, except for scrotal circumference.

The multivariate genetic variance-covariance structure is predominantly characterized by positive relationships among the four adult sheep traits.

two papers on stomata on needles

1. Stomatal Frequency and Size During Elongation of Tsuga heterophylla Needles

2. Growth and Nutrient Retranslocation in Needles of Radiata Pine in Relation to Nitrogen Supply

FASTX-Toolkit - for FASTA/FASTQ processing

FASTX-Toolkit -  preprocess the FASTA/FASTQ files before mapping the sequences to the genome

pypedia - a collaborative programming web environment on bioinformatics

http://www.pypedia.com/index.php/Main_Page

A

• Add polyphen annotation to SNPs from SeattleSeqAnnotation outputs user Kantale
• Align VCF to reference
• Align VCF to reference user Kantale
• Allele frequencies from VCF user Kantale
• Analyze beagle imputation Marchini user Kantale
• Analyze VCF diff filename user Kantale
• Annotatae variant list file via SeattleSeqAnnotation user Kantale
• Annotate list of chromosome position files with GO from BioMart Ensembl user Kantale
• Annotate variant list file via SeattleSeqAnnotation user Kantale
• Annotate VCF file via ANNOVAR user Kantale
• Annotate VCF file with GO user Kantale
• Annotate VCF file with GO user Test
• Annotate VCF filename via ANNOVAR user Kantale
• Annotate VCF filename with GO user Kantale
• ANNOVAR annotation user Kantale
• ANNOVAR Annotation user Kantale
• Assembly impute2 gprobs bins user Kantale

free statistical books

OpenIntro.org provides a free statistical textbook, along with data sets and supplementary materials.

 Ipsur.org is another one.

overtake, lag behind - 超过，落后

Since then, genomics has become even less of a national priority, and other countries have overtaken France. In terms of sequencing infrastructure, France lags behind its European peers Germany and the UK. 

link ecologic niche models with intra-specific genetic variation

Climate envelope modelling reveals intraspecific relationships among flowering phenology, niche breadth and potential range size in Arabidopsis thaliana

http://evol-eco.blogspot.de/2012/05/putting-ecological-niche-models-to-good.html