I guess that as a CAFA organizer, I should really contribute to the second page. And I will. But I really don’t mind if someone else jump-starts it.

http://en.wikipedia.org/wiki/Protein_function_prediction

http://en.wikipedia.org/wiki/Critical_Assessment_of_Function_Annotation

As soon as Rabbit was out of sight, Pooh remembered that he had forgotten to ask who Small was, and whether he was the sort of friend-and-relation who settled on one’s nose, or the sort who got trodden on by mistake, and as it was Too Late Now, he thought he would begin the Hunt by looking for Piglet, and asking him what they were looking for before he looked for it.
“And it’s no good looking at the Six Pine Trees for Piglet,” said Pooh to himself, “because he’s been organdized in a special place of his own. So I shall have to look for the Special Place first. I wonder where it is.”
And he wrote it down in his head like this:

ORDER OF LOOKING FOR THINGS

Special Place Piglet Small Rabbit Small again

(To find Piglet) (To find who Small is) (To find Small) (To tell him I’ve found Small) (To tell him I’ve found Rabbit)

“Which makes it look like a bothering sort of day,” thought Pooh as he stumped along.

Of course, it does turn out to be a bothering sort of day, and nothing goes according to plan. Pooh does find Small but that is almost an afterthought considering the other things he discovered that day.

Just like science. You set out looking for something, you find a bunch of other things. You may or may not find what you  to originally set out to look for, but by the time you get to finding Small, finding him may not be the accomplishment you originally thought it may be. Something else has superseded it.

I started writing this post about the search for the smallest organism. Why? Because life in small packages fascinates me. How small can a biological package be, and still be considered living? Or: “The Search for Small(est)”.

But like Pooh, I bumbled along into other things.

So what is the smallest living thing? Starting at the smallest scale, viruses  are considered by most scientists to be replicators, rather than organisms. They do not metabolize, and do not carry a full complement of reproduction machinery. They affect life profoundly, but they are missing a few essential components to actually be living.  This view has been shaken up recently with the discovery of giant viruses that have genomes larger than some bacteria. These genomes are also quite complex, including coding for a large part of the reproductive machinery, having a selective membrane, and other of life’s goodies. Still, even if we consider giant viruses (or mimiviruses, as they are called)  have crossed the border between non-life and life and are considered to be living, they are already not the smallest around. Not in genome size, and not in the particle size. Indeed, mimivirus were, for a long time, mistaken for bacteria due to their size, which is where they go their name: “mimi”  is short for microbial mimic.

So: small bacteria? The bacterium Candidatus carsonella rudii is really small: its genome is just shy of 160,000 base pairs and it codes for about 182 predicted genes. But carsonella is an obligatory endosymbiont: it lives inside the cells of a special organ in the jumping plant louse or psyllid, an insect that feeds on plant phloem. Carsonella cannot survive outside its host and, in fact, its genome has lost so many genes that it is practically an organelle, not much larger than a mitochondrion. (A mitochondrion  has 16,000 base-pairs and 32 genes.) Mitochondria are not living, although they originated from bacteria. Is carsonella there yet? Has it crossed into from life into non-life just as mimiviruses may have crossed from life into life? Buchnera, another insect endosymbiont is not much larger, with about 400 genes.

Mycoplasma genitalium is parasitic,  but at least it codes for (almost) all of its proteins. It is usually heralded as “the smallest organism that can be grown in cell-free culture”. Its genome is  521 genes strong: just 3 times more than that of carsonella. It is not an obligatory endosymbiont, but it is a parasite: we can trick it to live and grow in a nutrient-rich soup, but in nature you will not find it outside a host.

Pelagibacter ubique, a marine bacterium, is, as far as we know, the smallest free-living organism, with approximately 1390 predicted genes.

So in searching for Small, I was asking a question that seemed to become more awkward each time I thought I found him: is this Small I found  living or not?  Each of the Smalls has certain characteristics of life, but where on the scale outlined by pelagibacter, mycoplasma, carsonella and a mitochondrion does life turn into non-life?

When a question you ask makes you feel weird, you may want to consider whether you are asking the right question. So maybe I was asking the wrong question. Maybe the definition of life is not a binary one and we should not think in terms of “living” and “not living”. Life may very well be a quantitative thing.  Life sheds itself into non-life gradually, from free-living to parasitic to endiosymbiont to organelle.  Indeed, self-replicating proteins (prions) and self-replicating RNA (viroids) are the byproduct of much more complex life, which has arisen before those replicators were derived. As they are, they are non-living, but they owe their existence to life.

So there is no single boundary where “life”  crosses over to “non-life”. That’s not the right way to look at it. When journeying  from virus through mimivirus,  through organelle, various endosymbionts, parasites to free-living we are are simply hitting milestones on a continuum. Perhaps not that different from the continuum from which life emerged in the first place.

Understanding this probably beats actually finding Small.

Prepared by daughter. Not to scale. Species not yet identified. Delicious.

Together with obesity, insulin resistance is the harbringer of metabolic syndrome. Insulin resistance is when the body cannot use insulin effectively. Insulin is needed to help control the amount of sugar in the body. As a result, blood sugar and fat levels rise.  Therein lies the path to morbid obesity, diabetes, stroke, and heart problems.

So what’s the connection of metabolic disease to bacteria? Well, for one thing, we know that in obese people the bacterial population in the gut is different, and the different population of bacteria may lead to a vicious cycle contributing to obesity.

Another possible connection has to do with an  important group of molecules in our body called Toll-like receptors, or  TLRs. TLRs are a family of  membrane proteins that sense a wide variety of bacterial populations and activate our innate immune system. TLRs are like a first-defense warning station: they sense the bacterial enemy first, and, if needed, activate the proper defense mechanisms. Researchers studying TLR-2 have created knockout mice lacking TLR-2, and they discovered is that many of TLR-2 knockout mice do not develop insulin resistance when fed with a high-fat diet. Think about it: all the McCrap you can eat, yet your blood sugar level remains normal (although you still grow fat).  So why does that happen? How come these mice lacking a bacterial sensor also seem immune to insulin resistance?

To answer this, we must understand that TLR receptors (quite a few are known so far) are known to serve as a bridge (or “mediate crosstalk”) between the immune system and the body’s metabolism.  Mice without TLR-5 develop eating disorder known as hyperphagia which is characterized by an increased appetite; they also show other pre-diabetic  symptoms: hypertension, high lipids, and insulin resistance. I have posted before about how TLR-5 may control the type of gut bacteria mice have and, in turn, control their propensity for obesity.

TLR-4 deficient mice, on the other hand, seem to be protected from insulin resistance, just like TLR-2 deficient mice. So a connection between these front-line sensors of the immune system and whole body metabolism is well-known.

A group of researchers from Brazil have decided to look further into these “diabetes resistant” mice. The thing about mutant TLR deficient mice, is that they are normally grown in sterile conditions because possible infections and because the uncontrollable gut microbes add uncontrolled variables to any experiment. When scientists cannot precisely control for experimental conditions, they face two choices: One, they can deviate from the model to emulate “real world” better, but sacrifice control of one or more of the variables in an experiment. Or, maintain full control of the experiment and sacrifice a simulation whatever they are trying to model. Most scientists go with the second option: they would prefer to have a well-controlled model, even if it supposedly detracts from its supposed practicality and application to “real life”. That is because a model (in our case, mutant TLR mice), is somewhat removed from the real thing anyway: mutant TLR-deficient are basically a  an artificial construct used to investigate the effect of knocking out a TLR from the mouse, so hopefully we can draw conclusions about humans. So the second type of possible error is the one scientists generally prefer to make.

Toll-like receptors: it's complicated.

But Andrea Caricilli and colleagues have decided to look at TLR-2 knockout mice in non-sterile conditions. Rememebr: TLR-2 knockouts seem to have protection from insulin resistance. What Caricilli and her colleagues discovered was quite the opposite of what was known so far: TLR-2 knockout mice were not protected from insulin resistance. Quite the opposite: the mutant mice developed metabolic syndrome. But did  the gut bacteria do it? To check that, the researchers treated the mice with broad-spectrum antibiotics for 20 days. After that, the bacterial species that re-colonized the mice’s guts were quite different in their composition from the bacterial species that originally inhabited them. And they did not have meteabolic disease, or the symptoms were much less severe.

So here it is: changing the mice’s gut microbiota changed them from mice with insulin resistance to mice without insulin resistance. Yes, there are mutant mice, but still: insulin resistance was turned off  by changing the types of microbes in the gut.

They then transplanted the microbiota from TLR-2 mutants which had insulin resistance to regular mice. And what do you know: the regular mice then showed symptoms of insulin resistance and metabolic disease.

There is a lot more to this paper than these two experiments: they have also investigated many other parameters, trying to come up with the chain of events that bacteria trigger when causing metabolic disease. I won’t get into that, the paper is quite long with some 20(!)  figures. A lot of work went into this. But the bottom line again supports what has been shown in other studies: the bacteria that live in our gut are responsible for our metabolism, and it is the interaction between the bacteria and our immune system that not only protects us from pathogens, but also protects us (or not) from metabolic disease.

 Update: this post has been slashdotted. Exercise extreme caution.

Caricilli, A., Picardi, P., de Abreu, L., Ueno, M., Prada, P., Ropelle, E., Hirabara, S., Castoldi, A., Vieira, P., Camara, N., Curi, R., Carvalheira, J., & Saad, M. (2011). Gut Microbiota Is a Key Modulator of Insulin Resistance in TLR 2 Knockout Mice PLoS Biology, 9 (12) DOI: 10.1371/journal.pbio.1001212

1. Men don’t tell the truth about their penis. No kidding? But this is somewhat more serious. It has been accepted for some time that male circumcision dramatically reduces the rate of HIV infection. But recently, some reports have shown that high rates of infection prevail among circumcised men as well. But since circumcision is usually self-reported, could there be a problem there? This study shows that in a cross-sectional (sorry…) study among recruits to the Lesotho Defense Force, 50% of the men that reported they were circumcised were, in fact, partially (27%) or completely (23%) not circumcised. The researchers conclude that biases in the self-reporting of male circumcision may lead to erroneous reports that show high HIV infection rates among circumcised men.

Concluding quote:

…until further research can document improved methods for obtaining accurate self-reported MC [male circumcision I.F.] data, all assessments of MC and HIV prevalence, as well as projections for VMMC [voluntary male medical circumcision I.F.] interventions, should be informed by physical-exam-based data [as opposed toself reporting, I.F.].

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0027561

Can sharing data help prevent errors and fraud?

From the abstract:

Background: The widespread reluctance to share published research data is often hypothesized to be due to the authors’ fear that reanalysis may expose errors in their work or may produce conclusions that contradict their own. However, these hypotheses have not previously been studied systematically

So Jelte Wicherts and his colleagues from the University of Amsterdam wanted to see whether sharing data was related to the number of statistical analysis errors in a paper. So, to phrase this as a null and alternative hypothesis:

H0:There is no difference in the number of statistical errors in those papers where the authors are willing to share data, and those where the authors are unwilling to do so.

H1: (one sided): the number of weaker evidence and statistical errors in papers where the authors are unwilling to share data is larger than those in which the authors are willing to share data.

Wicherts and colleagues contacted authors of 141 papers published in five journals of the American Psychological Association, requesting their data. Trouble is, they could not get enough authors to share data to make their own study significant: in a previous study, some 73% of the authors contacted were unwilling to share data. Wow.

However, authors publishing in two of these journals, Journal of Personality and Social Psychology (JPSP) and Journal of Experimental Psychology: Learning, Memory, and Cognition (JEP:LMC), were somewhat more forthcoming.  Wicherts and colleagues therefore limited their analysis to a subset of 49 papers published in those journals. (Note that sometimes lack of data sharing is due to legitimate considerations, such as being part of an ongoing study, or third-party proprietary rights. However, those were not considerations in 49 papers analyzed here.)

Wicherts  then checked for specific types of statistical errors in these papers, and compared the number of errors in papers from authors willing to share data to those who did not. Here are some of the findings:

Distribution of the number of errors in the reporting of p-values for 28 papers from which the data were not shared (left column) and 21 from which the data were shared (right column) for all misreporting errors (upper row), larger misreporting errors at the 2nd decimal (middle row), and misreporting errors that concerned statistical significance (p<.05; bottom row). doi:10.1371/journal.pone.0026828.g001

Pretty clear picture: those papers where the authors authors were willing to share data were less prone to statistical errors.

Concluding quote:

In this sample of psychology papers, the authors’ reluctance to share data was associated with more errors in reporting of statistical results and with relatively weaker evidence (against the null hypothesis). The documented errors are arguably the tip of the iceberg of potential errors and biases in statistical analyses and the reporting of statistical results. It is rather disconcerting that roughly 50% of published papers in psychology contain reporting errors [33] and that the unwillingness to share data was most pronounced when the errors concerned statistical significance.

Although note that Wicherts is very careful about drawing conclusions:

Although our results are consistent with the notion that the reluctance to share data is generated by the author’s fear that reanalysis will expose errors and lead to opposing views on the results, our results are correlational in nature and so they are open to alternative interpretations. Although the two groups of papers are similar in terms of research fields and designs, it is possible that they differ in other regards. Notably, statistically rigorous researchers may archive their data better and may be more attentive towards statistical power than less statistically rigorous researchers. If so, more statistically rigorous researchers will more promptly share their data, conduct more powerful tests, and so report lower p-values. However, a check of the cell sizes in both categories of papers (see Text S2) did not suggest that statistical power was systematically higher in studies from which data were shared.

In fact, Wicherts also wrote a piece in Nature where he argued that sharing data can help avoid fraud, such as in the recent infamous case of Diederik Stapel, a highly regarded psychologist at Tilburg University in the Netherlands.

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0026828

3. Toilet paper. A study of surfaces of public restrooms has shown that they are covered with bacteria, mainly the kind that is known to live on and in humans. So now we have a somewhat broader view of the species living in restrooms, including the uncultured ones.

Two interesting quotes from the paper:

Although many of the source-tracking results evident from the restroom surfaces sampled here are somewhat obvious, this may not always be the case in other environments or locations.

Not sure about this bit: if the sources here are obvious, then is this paper a proof-of concept?

Also:

Unfortunately, previous studies have documented that college students (who are likely the most frequent users of the studied restrooms) are not always the most diligent of hand-washers.

No shit! (Pun intended).

Concluding quote:

Although the methods used here did not provide the degree of phylogenetic resolution to directly identify likely pathogens, the prevalence of gut and skin-associated bacteria throughout the restrooms we surveyed is concerning since enteropathogens or pathogens commonly found on skin (e.g. Staphylococcus aureus) could readily be transmitted between individuals by the touching of restroom surfaces.

Translation:

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0028132

Thomas, A., Tran, B., Cranston, M., Brown, M., Kumar, R., & Tlelai, M. (2011). Voluntary Medical Male Circumcision: A Cross-Sectional Study Comparing Circumcision Self-Report and Physical Examination Findings in Lesotho PLoS ONE, 6 (11) DOI: 10.1371/journal.pone.0027561

Wicherts, J., Bakker, M., & Molenaar, D. (2011). Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results PLoS ONE, 6 (11) DOI: 10.1371/journal.pone.0026828

Flores, G., Bates, S., Knights, D., Lauber, C., Stombaugh, J., Knight, R., & Fierer, N. (2011). Microbial Biogeography of Public Restroom Surfaces PLoS ONE, 6 (11) DOI: 10.1371/journal.pone.0028132

Man is the only animal that laughs and weeps, for he is the only animal that is struck with the difference between what things are and what they ought to be.
– William Hazlitt

We like to think that we are the only species capable of emotional self-awareness and therefore the only “animal that laughs and weeps”, but that is quite probably untrue, as other animals have been shown to laugh and perhaps weep.

Credit: Shiny Things, Flickr

Whatever that elusive quality is that distinguishes us from our closest cousins, the chimps and the bonobos, it is to be found in our genome. Since human and some great apes and other primate genomes have been sequenced, the basis for comparing these blueprints exists. Many studies have been done comparing the conservation of genes, copy numbers of genes, intergenic regions, control regions, synteny, splicing and other mechanisms that may explain the differences between us and our 96% cousins. As expected, no one factor can  explain why bonobos are peaceful and sexual, chimps are aggressive and patriarchal, and humans worry about taxes and blog.

Are there any new genes in humans that can help explain these differences? New genes can arise in various ways: gene duplication, exon shuffling, horizontal transfer, genes may split up (fission) or merge (fusion).

But how about genes that are completely new in humans? Do we have genes that we can claim as our own and are neither homologous to those in other apes nor have arisen from a mix & match manipulation in the common lineage of all apes? Are there actually human genes that are just that: exclusively human?

A group from China and Canada has decided to tackle that question. They looked specifically for genes that are new in the human lineage, but not in chimp or orangutan. (I’m not exactly sure why they did not look in Gorilla too, which is the other great ape with a mostly sequenced genome, perhaps because the assembly is still very much in progress.)

So how does one go about looking for genes that are human-only? The pipeline Wu and colleagues have set up looks like this:

Clockwise, from top left:

1. They scanned the human genome   for genes with a high similarity in the genomes of chimp, orangutan and rhesus macaque. That left them with 584 genes (out of roughly 25,000) which did not have an ortholog in other primates.

2. A simple sanity check: those human genes with no start or stop codons were probably mis-identified. We are now down to 352 genes.

3. Of the 352, they looked for those that have disrupted homologous regions in chimp and/or orangutan. That mans that while the gene is functional in humans, it is not functional in the other primates. Disrupted homologous regions can mean that in non-humans the gene does not have a start codon, or has a premature stop codon, or has some frameshift mutation that renders it non-translatable. From 352 we are now down to 66 new human gene candidates.

4. But a human gene, even if not functional in other primates, may have been functional in a common ancestor of all primates, lost in the orangutan and chimp lineages, but maintained in humans. This history not make the gene as brand-new human-only. So in the 66 remaining genes they looked for sequences where the mutation that rendered them functional (like an ATG start codon, or the removal of a missense mutation) was found only in humans. Now we are left with 46 genes.

5. Great, so we have 46 open reading frames in humans that look like original, human-lineage only genes. But are they functional? Do they actually transcribe into RNA and translate into protein? (RNA-only genes were excluded from this rather conservative pipeline, they are hard enough to identify as it is.)  To find that out, they looked for transcribed regions EST databases (for RNA), and in the PRIDE peptide database (for protein). Now we are left with 27 genes that are novel in humans, and because they are translated are probably active.

Trouble is, some of these genes are listed only in certain versions of Ensembl, the genome database from which the researchers took their data; (they used version 56.) This highlights a problem with the annotation of genes with no homologs: their annotation is volatile, and may change between different versions of the same database of the exact same genome. To overcome this problem, the researchers subjected different versions of Ensembl (40 through 55) to the same pipeline described above. They discovered an additional 33 genes that are candidates for de novo  human-lineage only active genes, bringing the total up to 60.

What are those genes like?  Why are they found only in humans? Can they help explain the differences between human and other primates? Well, for one, they’re short. Only one or, at most, two exons. This makes sense as these relatively new genes had not the time to accumulate splice sites.

The researchers moved on to look where the genes were expressed. They used RNA-Seq data from 11 different human tissues: adipose, whole brain, cerebral cortex, breast, colon, heart, liver, lymph node, skeletal muscle, lung and testes.

Here is what they found:

Panel C is the  business bit: the expression of the 60 de novo  human genes normalized by the general expression levels of genes in those tissues. (Pray, where are the error bars?). Seems like in Woody Allen’s two favorite organs, the testes and the cerebral cortex, do these genes have the highest expression. This actually makes some sort of sense: the testes are hypothesized to be a hotbed (sorry…) of evolutionary novelty, with all the meiosis going on there. The  high expression of the de-novo human genes in the cerebral cortex also seems to confirm our anthropomorphic prejudice: we are smarter. Yay. EDIT: Following MRR’s comment: yes, we should check de-novo genes and their expression in chimps. Perhaps the high expression of  de-novo genes exclusive to chimp lineage is in the cerebral cortex and testes too.

The authors do point out that there may be many other de-novo human lineage genes:

Our estimated rate, though, for de novo origin may be underestimated due to the conservativeness of our pipeline. First, as described above, in our pipeline, translatable open reading frames must have been complete in the human genome and disrupted in both the chimpanzee and orangutan genomes to be candidates as a de novo gene. Genes that did not have a clear ortholog (i.e., a sequence with very high similarity) in either the chimpanzee or the orangutan genomes (both of which are less complete than the human genome, and thus could be a missing genes) were not used. It is also often difficult to determine whether a protein-coding gene originated specifically on the human lineage or if it originated in a primate ancestor but was then lost on both the chimpanzee and orangutan lineages. The conservativeness of our pipeline thus only allowed us to accept genes where we could clearly show human specific mutations generated complete protein-coding reading frames, and that these were conserved for disrupting state in both the chimpanzee and orangutan genomes. As both the chimpanzee and orangutan sequences should be non-functional sequences, and thus not under selection, there is a reasonable likelihood that a second mutation, in addition to the human open reading frame completing mutation, could have occurred in the chimpanzee or orangutan that would prevent us for identifying these genes as having a de novo origin on the human lineage.

Also, PRIDE and PeptideAtlas, the databases of proteins they used may be underpopulated, and not include many other proteins.

To conclude, yes, humans do have their own brand-new genes which, together with many other genomic features, may help explain the differences between humans and other primates. And there are probably more of these genes than we have found so far.

As for what it means to be human:

Far out in the uncharted backwaters of the unfashionable end of the Western Spiral arm of the Galaxy lies a small unregarded yellow sun. Orbiting this at a distance of roughly ninety-eight million miles is an utterly insignificant little blue-green planet whose ape-descended life forms are so amazingly primitive that they still think digital watches are a pretty neat idea.

Perhaps it was the late, great Douglas Adams who nailed it.

Wu, D., Irwin, D., & Zhang, Y. (2011). De Novo Origin of Human Protein-Coding Genes PLoS Genetics, 7 (11) DOI: 10.1371/journal.pgen.1002379

g    Move pointer to the right.
e    Move pointer to the left.
n    Increment the cell at the pointer.
o    Decrement the cell at the pointer.
m    Jump forward past the matching i if the cell at the current pointer is zero.
i    Jump backward to the matching m unless the cell at the current pointer is zero.
c    Output the value of the cell at the pointer.
s    Input a byte and store it in the cell at the pointer.

As you can probably tell, I spent a lot of time working on genomics, but out pure generosity I am placing this incredibly useful language in the public domain. I’m sure we will see a BioGenomics group on Open Bioinformatics Forum any day now, and that genomics will prove to be a game-changer in the field of, um, genomics.

Allow me to end this post with the following inspirational statement:

nnnnnnnnnnmgnnngnnnnnngnnnnnnnngnnnnnnnnnngnnnnnnnnnnneeeeeoiggnnnnnnnn
nnnnncenncgggnnnnnnnncgncnnnnnnnceoooooooceeecgggnncoocgoooooooocncennn
nnnnncoooocennnnnnnnnnnnnnnnnnncggnnnnceeeencoooooooooooooooooooooooc

Thank you.

from Bio import SeqIO
from itertools import izip_longest
# Loop over pairs of reads
readiter = SeqIO.parse(open(inpath), "fastq")
for rec1, rec2 in izip_longest(readiter, readiter):
    print rec1.id  # do something with rec1
    print rec2.id  # do something with rec2
    .
    .

izip_longest is fed the same iterator, readiter, twice. However, readiter.next(), which advances the iterator, is called on the first argument and then on the second argument. Since next() is being called on the same iterator, successive records are yielded.

By “merged file” I mean a fastq file where the mate-pairs are one after the other, as in:

@HWUSI-EAS687_112864999:8:1:1980:1055#CGAGAA/1
GTTTGTTTTAATTTCAGTGATTCATCAATTTTAAAAAAAGATGAGAATAATAACTATTATAAAAAGATAAATAAATGTGAAATTTATATTTCAAATTCAA
+
@:DGBGDDD@GGGDGDGDDGD@GGGGE@GGG?EBGGGADDDDGEG4?3BA*::7:GEGGGG>EDDDDAG@G><ADDGBGGGGEGGGGDGGGFEGGGEFDE
@HWUSI-EAS687_112864999:8:1:1980:1055#CGAGAA/2
AATGAATTGAATAAATATAAGAAGGATGATTAATAATAATTCTTGAATTTGAAATATAAATTTCACATTTATTTATCTTTTTATAATAGTTATTATTCTC
+
D?DB:@8EBDB>GG:=<DED79>>A8CEC8DGDGG8CEC<BGGG+BAAEA@D<2D71;:8AG<ABBEEEEBEDC?C>AACDDDCD>AD<@EFFDDDECBB
@HWUSI-EAS687_112864999:8:1:2274:1058#CGAGAA/1
CCTCAGTTAGCTTCTATTGGTATTAACATGGGTGAATTTACTAAACAATTTAATGACCAAACTAAAGATAAAAATGGTGAAGTTATACCTTGTATAATTA
+
GFGGGHHGHHHHHHGHHHHHGHHHHHHHFBGDBGEHHHHFHHEHHHHDFHCGFFFHHHHHHHGHHGGEBHEEFFCEE@E>A>>8A@EBE@BBB>BGEEDB
@HWUSI-EAS687_112864999:8:1:2274:1058#CGAGAA/2
AACTGGAGTTGTTTTAATTTCAAAAGTAAAAGATTTATCTTTAAATGCTGTAATTATACAAGGTATAACTTCACCATTTTTATCTTTAGTTTGGTCATTA
+
IIIIIIIIIIGIIIDHHIIIIDIHD8CGGGGDADEIIIIIIIHIIGBGD>DGDGGDGIGIIIIBGDG@GFHIIII<C<CCGHHHIHIBGDEEB3BEDEE@

The solution is derived from this Stackoverflow entry.

Of course, if the mate-pair files are not merged then you can use this script to merge them. Also illustrates using iterators from two different files in one for loop:

#!/usr/bin/env python
from Bio import SeqIO
import itertools
import sys
import os
def merge_fastq(fastq_path1, fastq_path2, outpath):
    outfile = open(outpath,"w")
    fastq_iter1 = SeqIO.parse(open(fastq_path1),"fastq")
    fastq_iter2 = SeqIO.parse(open(fastq_path2),"fastq")
    for rec1, rec2 in itertools.izip(fastq_iter1, fastq_iter2):
        SeqIO.write([rec1,rec2], outfile, "fastq")
    outfile.close()

if __name__ == '__main__':
    outpath = "%s.merged.fastq" % os.path.splitext(sys.argv[1])[0]
    merge_fastq(sys.argv[1],sys.argv[2],outpath)

 

The Dead Body That Claims It Isn’t: I’m not dead.
The Dead Collector: What?
Large Man with Dead Body: Nothing. There’s your ninepence.
The Dead Body That Claims It Isn’t: I’m not dead.
The Dead Collector: ‘Ere, he says he’s not dead.
Large Man with Dead Body: Yes he is.
The Dead Body That Claims It Isn’t: I’m not.
The Dead Collector: He isn’t.
Large Man with Dead Body: Well, he will be soon, he’s very ill.
The Dead Body That Claims It Isn’t: I’m getting better.
Large Man with Dead Body: No you’re not, you’ll be stone dead in a moment.

– Monty Python and the Holy Grail

So it goes between John Cleese, Eric Idle and John Young.

But there is a sea, or rather lake, that is not dead yet either, feels happy, and is going for a walk on Thursday. The Dead Sea, located in the Judean Desert, roughly divided between Jordan and Israel, has a salinity of 33% – more than eight times that of seawater (3.5%). No animals or plants can survive these conditions, and the lake does look dead to the casual observer. But in the late 1930s Benjamin Elazari Volcani discovered that the Dead Sea does, in fact, support several types of microorganisms. A bit of history: Volcani’s research into microbial life in the Dead Sea led to him being awarded the the first doctoral degree in microbiology by the Hebrew University of Jerusalem, 1943. In 1975, Mullakhanbhai and Larsen named Halobacterium volcanii, a halophilic isolate, after B.E. Volcani. It is now know as the archaean, not bacterium, Haloferax volcanii.

At 33% salinity we all float. When you're down here with us, you'll float too.

Several other archeael isolates were found, noting that the Dead Sea is not quite dead yet. In fact, the lake sports what might be an underreported biodiversity. In addition to archaea it also has fungi, bacteria, protozoa and mermaids. OK, maybe not mermaids.

Now the Dead Sea has been found to be more alive than ever. A groups of Israeli and German divers have found freshwater springs deep in the Dead Sea. The springs are about 30m deep, and lie in of large craters 30meters in diameter. Look at the video below, taken by the divers. Between 1:54 and 2:10 you can see the freshwater mixing with the saltwater. The stark differences in salinity makes for a surreal underwater smoke effect. And, the real kicker, at 2:26 you can see a thick microbial mat, like gunk all over the rocks near the spring.

It would be very interesting to find out who, exactly, comprises this mat. As far as I know, this analysis has not been published yet. But the initial results are reported in National Geographic:

Preliminary analyses of samples collected in the craters suggest that the springs’ bacterial communities are very diverse—akin to what you’d find living on rocks in a regular saltwater sea, he added.

The top of the springs’ rocks are covered with green biofilms, which use both sunlight and sulfide—naturally occurring chemicals from the springs—to survive. Exclusively sulfide-eating bacteria coat the bottoms of the rocks in a white biofilm.

Not only have the organisms evolved in such a harsh environment, Ionescu speculates that the bacteria can somehow cope with sudden fluxes in fresh water and saltwater that naturally occur as water currents shift around the springs.

All I can say is: wow. Microbial mats in the Dead Sea, which we only find about now. The  Dead Sea thriving with whole carpets of life. Who’d've thunk?

ELAZARI-VOLCANI, B. (1943). Bacteria in the Bottom Sediments of the Dead Sea Nature, 152 (3853), 274-275 DOI: 10.1038/152274c0

Mullakhanbhai, M., & Larsen, H. (1975). Halobacterium volcanii spec. nov., a Dead Sea halobacterium with a moderate salt requirement Archives of Microbiology, 104 (1), 207-214 DOI: 10.1007/BF00447326

Buchalo, A., Nevo, E., Wasser, S., Oren, A., & Molitoris, H. (1998). Fungal life in the extremely hypersaline water of the Dead Sea: first records Proceedings of the Royal Society B: Biological Sciences, 265 (1404), 1461-1465 DOI: 10.1098/rspb.1998.0458

The Friedberg Lab is recruiting graduate students, for both Master’s and Ph.D.

WE ARE:  A dynamic young lab  interested in gene, gene cluster and genome evolution, understanding microbial communities and microbe-host interactions by metagenomic analyses, developing algorithms for understanding gene cluster evolution, and prediction of protein function from protein sequence and structure.

YOU ARE: an independent, hard-working problem-solving, energetic and motivated scientist-to-be. You have graduated or are about to graduate in computer science and/or biology or related fields. The Friedberg Lab is a “dry” lab, so some programming skills are required (Python preferred).

Existing and planned projects include:

1. Computational protein function prediction and assessment of function prediction algorithms. The Friedberg Lab is among the leaders of the Critical Assessment of Function Annotations (CAFA), an international effort of dozens of research groups to asess and improve function prediction algorithms. We are looking for students that are excited about prediction of protein function from sequence and structure. Also, how well can we assess how well our algorithms are doing? The next CAFA meeting will take place in Berlin, July 2013 and the Friedberg Lab will play a central role in  answering these questions.

2. Metagenomics:  we are studying the interaction between the microbiome and the host using metagenomic and metatranscriptomic data. In collaboration We are looking at how the human microbiome affects gene expression in the host. Together with Robb Chapkin’s lab at Texas A&M we are analyzing microbial genomes and their effect on transcription in the human gut. We are also developing algorithms for context-based function prediction in metagenomic data. Simply put: how well can we prediction the function of a gene from its neighbors? Since many of the genes in metagenomic data have no known homologs, we are developing creative ways to computationally discover their function.

3. Microbial Evolution: we are researching the evolution of Mycoplasma, a bacteria genus which serves us as model clade for understanding genome evolution. Mycoplasma have the smallest genomes of any organism, and being parasitic evolve quickly. Together with the Balish Lab we expect to sequence several new species and strains in the next year, and we are developing computational methods and a central community database  for analyzing the Mycoplasma tree of life. Besides the biological aspect, this project is also a great opportunity to get into web programming, database design, and learn how top design and code community-based scientific software. 

4. Biopython: Biopython is a set of freely available tools for biological computation written in Python by an international team of developers. It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics. If you would like to become a Biopython developer, part of an international community of open-source scientific software developers, the Friedberg Lab is the place for you. This option is especially attractive for Master’s students seeking to enter bioinformatics in Industry.

5. Insert your brilliant idea here! I love new projects!

The lab is equipped with its own 10-node cluster computer, several workstations, and has access to Miami University’s Supercomputing Center, and the Ohio Supercomputer Center at Ohio State University.  Students have an excellent research environment, and many opportunities to collaborate with labs on and off campus.

Students can apply to the Friedberg Lab via the following graduate programs at Miami University:

1. Microbiology (Master’s and PhD).

2. Cell, Molecular and Strcutural Biology (PhD only).

3. Computer Science (Master’s only).

You are welcome and encouraged  to inquire further. I love talking with prospective students. If you would like to set up a phone/Skype chat please send your CV to:

friedberg.lab.jobs ‘at gmail ‘dot’ com

Looking forward to hearing from you.

Iddo Friedberg, PhD

Assistant Professor, Microbiology and Computer Science (affiliate)

Miami University

Oxford, OH, USA